There are a few options:

Read all sheets directly into an ordered dictionary.

import pandas as pd

# for pandas version >= 0.21.0
sheet_to_df_map = pd.read_excel(file_name, sheet_name=None)

# for pandas version < 0.21.0
sheet_to_df_map = pd.read_excel(file_name, sheetname=None)

Read the first sheet directly into dataframe

df = pd.read_excel('excel_file_path.xls')
# this will read the first sheet into df

Read the excel file and get a list of sheets. Then chose and load the sheets.

xls = pd.ExcelFile('excel_file_path.xls')

# Now you can list all sheets in the file
xls.sheet_names
# ['house', 'house_extra', ...]

# to read just one sheet to dataframe:
df = pd.read_excel(file_name, sheetname="house")

Read all sheets and store it in a dictionary. Same as first but more explicit.

# to read all sheets to a map
sheet_to_df_map = {}
for sheet_name in xls.sheet_names:
    sheet_to_df_map[sheet_name] = xls.parse(sheet_name)
    # you can also use sheet_index [0,1,2..] instead of sheet name.

Thanks @ihightower for pointing it out way to read all sheets and @toto_tico for pointing out the version issue.

sheetname : string, int, mixed list of strings/ints, or None, default 0 Deprecated since version 0.21.0: Use sheet_name instead Source Link

Also have a look into the built-in DataFrame.filter function.

Minimalistic but greedy approach (sufficient for the given df):

df.filter(regex="[^BD]")

Conservative/lazy approach (exact matches only):

df.filter(regex="^(?!(B|D)$).*$")

Conservative and generic:

exclude_cols = ['B','C']
df.filter(regex="^(?!({0})$).*$".format('|'.join(exclude_cols)))

I had this problem today using any of concat, append or merge, and I got around it by adding a helper column sequentially numbered and then doing an outer join

helper=1
for i in df1.index:
    df1.loc[i,'helper']=helper
    helper=helper+1
for i in df2.index:
    df2.loc[i,'helper']=helper
    helper=helper+1
df1.merge(df2,on='helper',how='outer')

Another solution using DataFrame.apply(), with slightly less typing and more scalable when you want to join more columns:

cols = ['foo', 'bar', 'new']
df['combined'] = df[cols].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)

def trim(x):
    if x.dtype == object:
        x = x.str.split(' ').str[0]
    return(x)

df = df.apply(trim)

Use the pandas.DataFrame.rename funtion. Check this link for description.

data.rename(columns = {'gdp': 'log(gdp)'}, inplace = True)

If you intend to rename multiple columns then

data.rename(columns = {'gdp': 'log(gdp)', 'cap': 'log(cap)', ..}, inplace = True)

If anyone like me likes chainable data manipulation using the pandas dot notation (like piping), then the following may be useful:

df3 = df3.query('~index.duplicated()')

This enables chaining statements like this:

df3.assign(C=2).query('~index.duplicated()').mean()

The way via unlist and matrix seems a bit convoluted, and requires you to hard-code the number of elements (this is actually a pretty big no-go. Of course you could circumvent hard-coding that number and determine it at run-time)

I would go a different route, and construct a data frame directly from the list that strsplit returns. For me, this is conceptually simpler. There are essentially two ways of doing this:

1. as.data.frame – but since the list is exactly the wrong way round (we have a list of rows rather than a list of columns) we have to transpose the result. We also clear the rownames since they are ugly by default (but that’s strictly unnecessary!):

   `rownames<-`(t(as.data.frame(strsplit(text, '\\.'))), NULL)
   

2. Alternatively, use rbind to construct a data frame from the list of rows. We use do.call to call rbind with all the rows as separate arguments:

   do.call(rbind, strsplit(text, '\\.'))
   

Both ways yield the same result:

     [,1] [,2] [,3]  [,4]
[1,] "F"  "US" "CLE" "V13"
[2,] "F"  "US" "CA6" "U13"
[3,] "F"  "US" "CA6" "U13"
[4,] "F"  "US" "CA6" "U13"
[5,] "F"  "US" "CA6" "U13"
[6,] "F"  "US" "CA6" "U13"
…

Clearly, the second way is much simpler than the first.

If you just want to see all the columns you can do something of this sort as a quick fix

cols = data_all2.columns

now cols will behave as a iterative variable that can be indexed. for example

cols[11:20]

How to iterate efficiently

If you really have to iterate a Pandas dataframe, you will probably want to avoid using iterrows(). There are different methods and the usual iterrows() is far from being the best. itertuples() can be 100 times faster.

In short:

• As a general rule, use df.itertuples(name=None). In particular, when you have a fixed number columns and less than 255 columns. See point (3)
• Otherwise, use df.itertuples() except if your columns have special characters such as spaces or '-'. See point (2)
• It is possible to use itertuples() even if your dataframe has strange columns by using the last example. See point (4)
• Only use iterrows() if you cannot the previous solutions. See point (1)

Different methods to iterate over rows in a Pandas dataframe:

Generate a random dataframe with a million rows and 4 columns:

    df = pd.DataFrame(np.random.randint(0, 100, size=(1000000, 4)), columns=list('ABCD'))
    print(df)

1) The usual iterrows() is convenient, but damn slow:

start_time = time.clock()
result = 0
for _, row in df.iterrows():
    result += max(row['B'], row['C'])

total_elapsed_time = round(time.clock() - start_time, 2)
print("1. Iterrows done in {} seconds, result = {}".format(total_elapsed_time, result))

2) The default itertuples() is already much faster, but it doesn't work with column names such as My Col-Name is very Strange (you should avoid this method if your columns are repeated or if a column name cannot be simply converted to a Python variable name).:

start_time = time.clock()
result = 0
for row in df.itertuples(index=False):
    result += max(row.B, row.C)

total_elapsed_time = round(time.clock() - start_time, 2)
print("2. Named Itertuples done in {} seconds, result = {}".format(total_elapsed_time, result))

3) The default itertuples() using name=None is even faster but not really convenient as you have to define a variable per column.

start_time = time.clock()
result = 0
for(_, col1, col2, col3, col4) in df.itertuples(name=None):
    result += max(col2, col3)

total_elapsed_time = round(time.clock() - start_time, 2)
print("3. Itertuples done in {} seconds, result = {}".format(total_elapsed_time, result))

4) Finally, the named itertuples() is slower than the previous point, but you do not have to define a variable per column and it works with column names such as My Col-Name is very Strange.

start_time = time.clock()
result = 0
for row in df.itertuples(index=False):
    result += max(row[df.columns.get_loc('B')], row[df.columns.get_loc('C')])

total_elapsed_time = round(time.clock() - start_time, 2)
print("4. Polyvalent Itertuples working even with special characters in the column name done in {} seconds, result = {}".format(total_elapsed_time, result))

Output:

         A   B   C   D
0       41  63  42  23
1       54   9  24  65
2       15  34  10   9
3       39  94  82  97
4        4  88  79  54
...     ..  ..  ..  ..
999995  48  27   4  25
999996  16  51  34  28
999997   1  39  61  14
999998  66  51  27  70
999999  51  53  47  99

[1000000 rows x 4 columns]

1. Iterrows done in 104.96 seconds, result = 66151519
2. Named Itertuples done in 1.26 seconds, result = 66151519
3. Itertuples done in 0.94 seconds, result = 66151519
4. Polyvalent Itertuples working even with special characters in the column name done in 2.94 seconds, result = 66151519

This article is a very interesting comparison between iterrows and itertuples

# To do it for all names
df[] <- lapply( df, factor) # the "[]" keeps the dataframe structure
 col_names <- names(df)
# to do it for some names in a vector named 'col_names'
df[col_names] <- lapply(df[col_names] , factor)

Explanation. All dataframes are lists and the results of [ used with multiple valued arguments are likewise lists, so looping over lists is the task of lapply. The above assignment will create a set of lists that the function data.frame.[<- should successfully stick back into into the dataframe, df

Another strategy would be to convert only those columns where the number of unique items is less than some criterion, let's say fewer than the log of the number of rows as an example:

cols.to.factor <- sapply( df, function(col) length(unique(col)) < log10(length(col)) )
df[ cols.to.factor] <- lapply(df[ cols.to.factor] , factor)

This is how I did it in 2021

Let us say I have a csv sales.csv which has the following data in it:

sales.csv:

Order Name,Price,Qty
oil,200,2
butter,180,10

and to add more rows I can load them in a data frame and append it to the csv like this:

import pandas

data = [
    ['matchstick', '60', '11'],
    ['cookies', '10', '120']
]
dataframe = pandas.DataFrame(data)
dataframe.to_csv("sales.csv", index=False, mode='a', header=False)

and the output will be:

Order Name,Price,Qty
oil,200,2
butter,180,10
matchstick,60,11
cookies,10,120

This should do the trick, to produce the data frame you asked for, using only base R:

df <- data.frame(cond=c(rep("x", times=length(x)), 
                        rep("y", times=length(y))), 
                 rating=c(x, y))

df
  cond rating
1    x      1
2    x      2
3    x      3
4    y    100
5    y    200
6    y    300

However, from your initial description, I'd say that this is perhaps a more likely usecase:

df2 <- data.frame(x, y)
colnames(df2) <- c(x_name, y_name)

df2
  cond rating
1    1    100
2    2    200
3    3    300

[edit: moved parentheses in example 1]

Drop by index

Delete first, second and fourth columns:

df.drop(df.columns[[0,1,3]], axis=1, inplace=True)

Delete first column:

df.drop(df.columns[[0]], axis=1, inplace=True)

There is an optional parameter inplace so that the original data can be modified without creating a copy.

Popped

Column selection, addition, deletion

Delete column column-name:

df.pop('column-name')

Examples:

df = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6]), ('C', [7,8, 9])], orient='index', columns=['one', 'two', 'three'])

print df:

   one  two  three
A    1    2      3
B    4    5      6
C    7    8      9

df.drop(df.columns[[0]], axis=1, inplace=True) print df:

   two  three
A    2      3
B    5      6
C    8      9

three = df.pop('three') print df:

   two
A    2
B    5
C    8

It means:

'O'     (Python) objects

Source.

The first character specifies the kind of data and the remaining characters specify the number of bytes per item, except for Unicode, where it is interpreted as the number of characters. The item size must correspond to an existing type, or an error will be raised. The supported kinds are to an existing type, or an error will be raised. The supported kinds are:

'b'       boolean
'i'       (signed) integer
'u'       unsigned integer
'f'       floating-point
'c'       complex-floating point
'O'       (Python) objects
'S', 'a'  (byte-)string
'U'       Unicode
'V'       raw data (void)

Another answer helps if need check types.

If I understand you correctly, you can use a combination of Series.isin() and DataFrame.append():

In [80]: df1
Out[80]:
   rating  user_id
0       2  0x21abL
1       1  0x21abL
2       1   0xdafL
3       0  0x21abL
4       4  0x1d14L
5       2  0x21abL
6       1  0x21abL
7       0   0xdafL
8       4  0x1d14L
9       1  0x21abL

In [81]: df2
Out[81]:
   rating      user_id
0       2      0x1d14L
1       1    0xdbdcad7
2       1      0x21abL
3       3      0x21abL
4       3      0x21abL
5       1  0x5734a81e2
6       2      0x1d14L
7       0       0xdafL
8       0      0x1d14L
9       4  0x5734a81e2

In [82]: ind = df2.user_id.isin(df1.user_id) & df1.user_id.isin(df2.user_id)

In [83]: ind
Out[83]:
0     True
1    False
2     True
3     True
4     True
5    False
6     True
7     True
8     True
9    False
Name: user_id, dtype: bool

In [84]: df1[ind].append(df2[ind])
Out[84]:
   rating  user_id
0       2  0x21abL
2       1   0xdafL
3       0  0x21abL
4       4  0x1d14L
6       1  0x21abL
7       0   0xdafL
8       4  0x1d14L
0       2  0x1d14L
2       1  0x21abL
3       3  0x21abL
4       3  0x21abL
6       2  0x1d14L
7       0   0xdafL
8       0  0x1d14L

This is essentially the algorithm you described as "clunky", using idiomatic pandas methods. Note the duplicate row indices. Also, note that this won't give you the expected output if df1 and df2 have no overlapping row indices, i.e., if

In [93]: df1.index & df2.index
Out[93]: Int64Index([], dtype='int64')

In fact, it won't give the expected output if their row indices are not equal.

You can use the following if you want to specify tricky formats:

df['date_col'] =  pd.to_datetime(df['date_col'], format='%d/%m/%Y')

More details on format here:

• Python 2 https://docs.python.org/2/library/datetime.html#strftime-strptime-behavior
• Python 3 https://docs.python.org/3.7/library/datetime.html#strftime-strptime-behavior

The dplyr hybridized options are now around 30% faster than the Base R subset reassigns. On a 100M datapoint dataframe mutate_all(~replace(., is.na(.), 0)) runs a half a second faster than the base R d[is.na(d)] <- 0 option. What one wants to avoid specifically is using an ifelse() or an if_else(). (The complete 600 trial analysis ran to over 4.5 hours mostly due to including these approaches.) Please see benchmark analyses below for the complete results.

If you are struggling with massive dataframes, data.table is the fastest option of all: 40% faster than the standard Base R approach. It also modifies the data in place, effectively allowing you to work with nearly twice as much of the data at once.

A clustering of other helpful tidyverse replacement approaches

Locationally:

• index mutate_at(c(5:10), ~replace(., is.na(.), 0))
• direct reference mutate_at(vars(var5:var10), ~replace(., is.na(.), 0))
• fixed match mutate_at(vars(contains("1")), ~replace(., is.na(.), 0))
  • or in place of contains(), try ends_with(),starts_with()
• pattern match mutate_at(vars(matches("\\d{2}")), ~replace(., is.na(.), 0))

Conditionally:
(change just single type and leave other types alone.)

• integers mutate_if(is.integer, ~replace(., is.na(.), 0))
• numbers mutate_if(is.numeric, ~replace(., is.na(.), 0))
• strings mutate_if(is.character, ~replace(., is.na(.), 0))

The Complete Analysis -

Updated for dplyr 0.8.0: functions use purrr format ~ symbols: replacing deprecated funs() arguments.

Approaches tested:

# Base R: 
baseR.sbst.rssgn   <- function(x) { x[is.na(x)] <- 0; x }
baseR.replace      <- function(x) { replace(x, is.na(x), 0) }
baseR.for          <- function(x) { for(j in 1:ncol(x))
    x[[j]][is.na(x[[j]])] = 0 }

# tidyverse
## dplyr
dplyr_if_else      <- function(x) { mutate_all(x, ~if_else(is.na(.), 0, .)) }
dplyr_coalesce     <- function(x) { mutate_all(x, ~coalesce(., 0)) }

## tidyr
tidyr_replace_na   <- function(x) { replace_na(x, as.list(setNames(rep(0, 10), as.list(c(paste0("var", 1:10)))))) }

## hybrid 
hybrd.ifelse     <- function(x) { mutate_all(x, ~ifelse(is.na(.), 0, .)) }
hybrd.replace_na <- function(x) { mutate_all(x, ~replace_na(., 0)) }
hybrd.replace    <- function(x) { mutate_all(x, ~replace(., is.na(.), 0)) }
hybrd.rplc_at.idx<- function(x) { mutate_at(x, c(1:10), ~replace(., is.na(.), 0)) }
hybrd.rplc_at.nse<- function(x) { mutate_at(x, vars(var1:var10), ~replace(., is.na(.), 0)) }
hybrd.rplc_at.stw<- function(x) { mutate_at(x, vars(starts_with("var")), ~replace(., is.na(.), 0)) }
hybrd.rplc_at.ctn<- function(x) { mutate_at(x, vars(contains("var")), ~replace(., is.na(.), 0)) }
hybrd.rplc_at.mtc<- function(x) { mutate_at(x, vars(matches("\\d+")), ~replace(., is.na(.), 0)) }
hybrd.rplc_if    <- function(x) { mutate_if(x, is.numeric, ~replace(., is.na(.), 0)) }

# data.table   
library(data.table)
DT.for.set.nms   <- function(x) { for (j in names(x))
    set(x,which(is.na(x[[j]])),j,0) }
DT.for.set.sqln  <- function(x) { for (j in seq_len(ncol(x)))
    set(x,which(is.na(x[[j]])),j,0) }
DT.nafill        <- function(x) { nafill(df, fill=0)}
DT.setnafill     <- function(x) { setnafill(df, fill=0)}

The code for this analysis:

library(microbenchmark)
# 20% NA filled dataframe of 10 Million rows and 10 columns
set.seed(42) # to recreate the exact dataframe
dfN <- as.data.frame(matrix(sample(c(NA, as.numeric(1:4)), 1e7*10, replace = TRUE),
                            dimnames = list(NULL, paste0("var", 1:10)), 
                            ncol = 10))
# Running 600 trials with each replacement method 
# (the functions are excecuted locally - so that the original dataframe remains unmodified in all cases)
perf_results <- microbenchmark(
    hybrid.ifelse    = hybrid.ifelse(copy(dfN)),
    dplyr_if_else    = dplyr_if_else(copy(dfN)),
    hybrd.replace_na = hybrd.replace_na(copy(dfN)),
    baseR.sbst.rssgn = baseR.sbst.rssgn(copy(dfN)),
    baseR.replace    = baseR.replace(copy(dfN)),
    dplyr_coalesce   = dplyr_coalesce(copy(dfN)),
    tidyr_replace_na = tidyr_replace_na(copy(dfN)),
    hybrd.replace    = hybrd.replace(copy(dfN)),
    hybrd.rplc_at.ctn= hybrd.rplc_at.ctn(copy(dfN)),
    hybrd.rplc_at.nse= hybrd.rplc_at.nse(copy(dfN)),
    baseR.for        = baseR.for(copy(dfN)),
    hybrd.rplc_at.idx= hybrd.rplc_at.idx(copy(dfN)),
    DT.for.set.nms   = DT.for.set.nms(copy(dfN)),
    DT.for.set.sqln  = DT.for.set.sqln(copy(dfN)),
    times = 600L
)

Summary of Results

> print(perf_results)
Unit: milliseconds
              expr       min        lq     mean   median       uq      max neval
      hybrd.ifelse 6171.0439 6339.7046 6425.221 6407.397 6496.992 7052.851   600
     dplyr_if_else 3737.4954 3877.0983 3953.857 3946.024 4023.301 4539.428   600
  hybrd.replace_na 1497.8653 1706.1119 1748.464 1745.282 1789.804 2127.166   600
  baseR.sbst.rssgn 1480.5098 1686.1581 1730.006 1728.477 1772.951 2010.215   600
     baseR.replace 1457.4016 1681.5583 1725.481 1722.069 1766.916 2089.627   600
    dplyr_coalesce 1227.6150 1483.3520 1524.245 1519.454 1561.488 1996.859   600
  tidyr_replace_na 1248.3292 1473.1707 1521.889 1520.108 1570.382 1995.768   600
     hybrd.replace  913.1865 1197.3133 1233.336 1238.747 1276.141 1438.646   600
 hybrd.rplc_at.ctn  916.9339 1192.9885 1224.733 1227.628 1268.644 1466.085   600
 hybrd.rplc_at.nse  919.0270 1191.0541 1228.749 1228.635 1275.103 2882.040   600
         baseR.for  869.3169 1180.8311 1216.958 1224.407 1264.737 1459.726   600
 hybrd.rplc_at.idx  839.8915 1189.7465 1223.326 1228.329 1266.375 1565.794   600
    DT.for.set.nms  761.6086  915.8166 1015.457 1001.772 1106.315 1363.044   600
   DT.for.set.sqln  787.3535  918.8733 1017.812 1002.042 1122.474 1321.860   600

Boxplot of Results

ggplot(perf_results, aes(x=expr, y=time/10^9)) +
    geom_boxplot() +
    xlab('Expression') +
    ylab('Elapsed Time (Seconds)') +
    scale_y_continuous(breaks = seq(0,7,1)) +
    coord_flip()

Color-coded Scatterplot of Trials (with y-axis on a log scale)

qplot(y=time/10^9, data=perf_results, colour=expr) + 
    labs(y = "log10 Scaled Elapsed Time per Trial (secs)", x = "Trial Number") +
    coord_cartesian(ylim = c(0.75, 7.5)) +
    scale_y_log10(breaks=c(0.75, 0.875, 1, 1.25, 1.5, 1.75, seq(2, 7.5)))

A note on the other high performers

When the datasets get larger, Tidyr''s replace_na had historically pulled out in front. With the current collection of 100M data points to run through, it performs almost exactly as well as a Base R For Loop. I am curious to see what happens for different sized dataframes.

Additional examples for the mutate and summarize _at and _all function variants can be found here: https://rdrr.io/cran/dplyr/man/summarise_all.html Additionally, I found helpful demonstrations and collections of examples here: https://blog.exploratory.io/dplyr-0-5-is-awesome-heres-why-be095fd4eb8a

Attributions and Appreciations

With special thanks to:

• Tyler Rinker and Akrun for demonstrating microbenchmark.
• alexis_laz for working on helping me understand the use of local(), and (with Frank's patient help, too) the role that silent coercion plays in speeding up many of these approaches.
• ArthurYip for the poke to add the newer coalesce() function in and update the analysis.
• Gregor for the nudge to figure out the data.table functions well enough to finally include them in the lineup.
• Base R For loop: alexis_laz
• data.table For Loops: Matt_Dowle
• Roman for explaining what is.numeric() really tests.

(Of course, please reach over and give them upvotes, too if you find those approaches useful.)

Note on my use of Numerics: If you do have a pure integer dataset, all of your functions will run faster. Please see alexiz_laz's work for more information. IRL, I can't recall encountering a data set containing more than 10-15% integers, so I am running these tests on fully numeric dataframes.

Hardware Used 3.9 GHz CPU with 24 GB RAM

The more recent tidyverse way is to use the mutate_at function:

library(tidyverse)
library(magrittr)
set.seed(88)

data <- data.frame(matrix(sample(1:40), 4, 10, dimnames = list(1:4, LETTERS[1:10])))
cols <- c("A", "C", "D", "H")

data %<>% mutate_at(cols, funs(factor(.)))
str(data)
 $ A: Factor w/ 4 levels "5","17","18",..: 2 1 4 3   
 $ B: int  36 35 2 26
 $ C: Factor w/ 4 levels "22","31","32",..: 1 2 4 3
 $ D: Factor w/ 4 levels "1","9","16","39": 3 4 1 2
 $ E: int  3 14 30 38
 $ F: int  27 15 28 37
 $ G: int  19 11 6 21
 $ H: Factor w/ 4 levels "7","12","20",..: 1 3 4 2
 $ I: int  23 24 13 8
 $ J: int  10 25 4 33

Let's create a sample dataset and do a deep dive into exactly why OP's code didn't work.

Here's our sample data:

val df = Seq(
  ("Rockets", 2, "TX"),
  ("Warriors", 6, "CA"),
  ("Spurs", 5, "TX"),
  ("Knicks", 2, "NY")
).toDF("team_name", "num_championships", "state")

We can pretty print our dataset with the show() method:

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
| Warriors|                6|   CA|
|    Spurs|                5|   TX|
|   Knicks|                2|   NY|
+---------+-----------------+-----+

Let's examine the results of df.select(df("state")==="TX").show():

+------------+
|(state = TX)|
+------------+
|        true|
|       false|
|        true|
|       false|
+------------+

It's easier to understand this result by simply appending a column - df.withColumn("is_state_tx", df("state")==="TX").show():

+---------+-----------------+-----+-----------+
|team_name|num_championships|state|is_state_tx|
+---------+-----------------+-----+-----------+
|  Rockets|                2|   TX|       true|
| Warriors|                6|   CA|      false|
|    Spurs|                5|   TX|       true|
|   Knicks|                2|   NY|      false|
+---------+-----------------+-----+-----------+

The other code OP tried (df.select(df("state")=="TX").show()) returns this error:

<console>:27: error: overloaded method value select with alternatives:
  [U1](c1: org.apache.spark.sql.TypedColumn[org.apache.spark.sql.Row,U1])org.apache.spark.sql.Dataset[U1] <and>
  (col: String,cols: String*)org.apache.spark.sql.DataFrame <and>
  (cols: org.apache.spark.sql.Column*)org.apache.spark.sql.DataFrame
 cannot be applied to (Boolean)
       df.select(df("state")=="TX").show()
          ^

The === operator is defined in the Column class. The Column class doesn't define a == operator and that's why this code is erroring out. Read this blog for more background information about the Spark Column class.

Here's the accepted answer that works:

df.filter(df("state")==="TX").show()

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

As other posters have mentioned, the === method takes an argument with an Any type, so this isn't the only solution that works. This works too for example:

df.filter(df("state") === lit("TX")).show

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

The Column equalTo method can also be used:

df.filter(df("state").equalTo("TX")).show()

+---------+-----------------+-----+
|team_name|num_championships|state|
+---------+-----------------+-----+
|  Rockets|                2|   TX|
|    Spurs|                5|   TX|
+---------+-----------------+-----+

It worthwhile studying this example in detail. Scala's syntax seems magical at times, especially when method are invoked without dot notation. It's hard for the untrained eye to see that === is a method defined in the Column class!

See this blog post if you'd like even more details on Spark Column equality.

I also like itertuples()

for row in df.itertuples():
    print(row.A)
    print(row.Index)

since row is a named tuples, if you meant to access values on each row this should be MUCH faster

speed run :

df = pd.DataFrame([x for x in range(1000*1000)], columns=['A'])
st=time.time()
for index, row in df.iterrows():
    row.A
print(time.time()-st)
45.05799984931946

st=time.time()
for row in df.itertuples():
    row.A
print(time.time() - st)
0.48400020599365234

With pandas it can be done as:

If lakes is your DataFrame:

area_dict = lakes.to_dict('records')

Simple plot,

you can use:

df.plot(x='Date',y='adj_close')

Or you can set the index to be Date beforehand, then it's easy to plot the column you want:

df.set_index('Date', inplace=True)
df['adj_close'].plot()

If you want a chart with one series by ticker on it

You need to groupby before:

df.set_index('Date', inplace=True)
df.groupby('ticker')['adj_close'].plot(legend=True)

If you want a chart with individual subplots:

grouped = df.groupby('ticker')

ncols=2
nrows = int(np.ceil(grouped.ngroups/ncols))

fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12,4), sharey=True)

for (key, ax) in zip(grouped.groups.keys(), axes.flatten()):
    grouped.get_group(key).plot(ax=ax)

ax.legend()
plt.show()

Don't drop. Catch the opposite of what you want.

df = df.filter(regex='^((?!badword).)*$').columns

eldNew <- eld[-14,]

See ?"[" for a start ...

For ‘[’-indexing only: ‘i’, ‘j’, ‘...’ can be logical vectors, indicating elements/slices to select. Such vectors are recycled if necessary to match the corresponding extent. ‘i’, ‘j’, ‘...’ can also be negative integers, indicating elements/slices to leave out of the selection.

(emphasis added)

edit: looking around I notice How to delete the first row of a dataframe in R? , which has the answer ... seems like the title should have popped to your attention if you were looking for answers on SO?

edit 2: I also found How do I delete rows in a data frame? , searching SO for delete row data frame ...

Also http://rwiki.sciviews.org/doku.php?id=tips:data-frames:remove_rows_data_frame

You can use the csv module found in the python standard library to manipulate CSV files.

example:

import csv
with open('some.csv', 'rb') as f:
    reader = csv.reader(f)
    for row in reader:
        print row

You should use either indexing or the subset function. For example :

R> df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8)
R> df
  x y z u
1 1 2 3 4
2 2 3 4 5
3 3 4 5 6
4 4 5 6 7
5 5 6 7 8

Then you can use the which function and the - operator in column indexation :

R> df[ , -which(names(df) %in% c("z","u"))]
  x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6

Or, much simpler, use the select argument of the subset function : you can then use the - operator directly on a vector of column names, and you can even omit the quotes around the names !

R> subset(df, select=-c(z,u))
  x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6

Note that you can also select the columns you want instead of dropping the others :

R> df[ , c("x","y")]
  x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6

R> subset(df, select=c(x,y))
  x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6

With pandas >= 1.0 there is now a dedicated string datatype:

1) You can convert your column to this pandas string datatype using .astype('string'):

df['zipcode'] = df['zipcode'].astype('string')

2) This is different from using str which sets the pandas object datatype:

df['zipcode'] = df['zipcode'].astype(str)

3) For changing into categorical datatype use:

df['zipcode'] = df['zipcode'].astype('category')

You can see this difference in datatypes when you look at the info of the dataframe:

df = pd.DataFrame({
    'zipcode_str': [90210, 90211] ,
    'zipcode_string': [90210, 90211],
    'zipcode_category': [90210, 90211],
})

df['zipcode_str'] = df['zipcode_str'].astype(str)
df['zipcode_string'] = df['zipcode_str'].astype('string')
df['zipcode_category'] = df['zipcode_category'].astype('category')

df.info()

# you can see that the first column has dtype object
# while the second column has the new dtype string
# the third column has dtype category
 #   Column            Non-Null Count  Dtype   
---  ------            --------------  -----   
 0   zipcode_str       2 non-null      object  
 1   zipcode_string    2 non-null      string  
 2   zipcode_category  2 non-null      category
dtypes: category(1), object(1), string(1)

The 'string' extension type solves several issues with object-dtype NumPy arrays:

1. You can accidentally store a mixture of strings and non-strings in an object dtype array. A StringArray can only store strings.

2. object dtype breaks dtype-specific operations like DataFrame.select_dtypes(). There isn’t a clear way to select just text while excluding non-text, but still object-dtype columns.

3. When reading code, the contents of an object dtype array is less clear than string.
   

More info on working with the new string datatype can be found here: https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html

dplyr::as_data_frame(df, rownames = "your_row_name") will give you even simpler result.

I prefer the oneliner:

print(sorted(df['Column Name'].unique()))

>>> import pandas as pd
>>> df = pd.DataFrame({'x' : [1, 2, 3, 4], 'y' : [4, 5, 6, 7]})
>>> df
   x  y
0  1  4
1  2  5
2  3  6
3  4  7
>>> s = df.ix[:,0]
>>> type(s)
<class 'pandas.core.series.Series'>
>>>

===========================================================================

UPDATE

If you're reading this after June 2017, ix has been deprecated in pandas 0.20.2, so don't use it. Use loc or iloc instead. See comments and other answers to this question.

You want rows where that condition is true so you need a comma:

data[data$Ozone > 14, ]

Detailed Example of Normalization Methods

• Pandas normalization (unbiased)
• Sklearn normalization (biased)
• Does biased-vs-unbiased affect Machine Learning?
• Mix-max scaling

References: Wikipedia: Unbiased Estimation of Standard Deviation

Example Data

import pandas as pd
df = pd.DataFrame({
               'A':[1,2,3],
               'B':[100,300,500],
               'C':list('abc')
             })
print(df)
   A    B  C
0  1  100  a
1  2  300  b
2  3  500  c

Normalization using pandas (Gives unbiased estimates)

When normalizing we simply subtract the mean and divide by standard deviation.

df.iloc[:,0:-1] = df.iloc[:,0:-1].apply(lambda x: (x-x.mean())/ x.std(), axis=0)
print(df)
     A    B  C
0 -1.0 -1.0  a
1  0.0  0.0  b
2  1.0  1.0  c

Normalization using sklearn (Gives biased estimates, different from pandas)

If you do the same thing with sklearn you will get DIFFERENT output!

import pandas as pd

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()


df = pd.DataFrame({
               'A':[1,2,3],
               'B':[100,300,500],
               'C':list('abc')
             })
df.iloc[:,0:-1] = scaler.fit_transform(df.iloc[:,0:-1].to_numpy())
print(df)
          A         B  C
0 -1.224745 -1.224745  a
1  0.000000  0.000000  b
2  1.224745  1.224745  c

Does Biased estimates of sklearn makes Machine Learning Less Powerful?

NO.

The official documentation of sklearn.preprocessing.scale states that using biased estimator is UNLIKELY to affect the performance of machine learning algorithms and we can safely use them.

From official documentation:

We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.

What about MinMax Scaling?

There is no Standard Deviation calculation in MinMax scaling. So the result is same in both pandas and scikit-learn.

import pandas as pd
df = pd.DataFrame({
               'A':[1,2,3],
               'B':[100,300,500],
             })
(df - df.min()) / (df.max() - df.min())
     A    B
0  0.0  0.0
1  0.5  0.5
2  1.0  1.0


# Using sklearn
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler() 
arr_scaled = scaler.fit_transform(df) 

print(arr_scaled)
[[0.  0. ]
 [0.5 0.5]
 [1.  1. ]]

df_scaled = pd.DataFrame(arr_scaled, columns=df.columns,index=df.index)
print(df_scaled)
     A    B
0  0.0  0.0
1  0.5  0.5
2  1.0  1.0

one liner using map, but if you'd like to specify additional args, you could do:

import pandas as pd
import glob
import functools

df = pd.concat(map(functools.partial(pd.read_csv, sep='|', compression=None), 
                    glob.glob("data/*.csv")))

Note: map by itself does not let you supply additional args.

If you have a dataset named daily_data:

daily_data<-daily_data[order(as.Date(daily_data$date, format="%d/%m/%Y")),] 

Do we have java syntax corresponding to below process

val dfResults = dfSource.select(concat_ws(",",dfSource.columns.map(c => col(c)): _*))

Joining fails if the DataFrames have some column names in common. The simplest way around it is to include an lsuffix or rsuffix keyword like so:

restaurant_review_frame.join(restaurant_ids_dataframe, on='business_id', how='left', lsuffix="_review")

This way, the columns have distinct names. The documentation addresses this very problem.

Or, you could get around this by simply deleting the offending columns before you join. If, for example, the stars in restaurant_ids_dataframe are redundant to the stars in restaurant_review_frame, you could del restaurant_ids_dataframe['stars'].

If you want to split by values in one of the columns, you can use lapply. For instance, to split ChickWeight into a separate dataset for each chick:

data(ChickWeight)
lapply(unique(ChickWeight$Chick), function(x) ChickWeight[ChickWeight$Chick == x,])

Here's a simple and dumb solution:

>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df = df.append({'foo':1, 'bar':2}, ignore_index=True)

Window functions:

Something like this should do the trick:

import org.apache.spark.sql.functions.{row_number, max, broadcast}
import org.apache.spark.sql.expressions.Window

val df = sc.parallelize(Seq(
  (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3),
  (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3),
  (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (2,"cat68",9.8),
  (3,"cat8",35.6))).toDF("Hour", "Category", "TotalValue")

val w = Window.partitionBy($"hour").orderBy($"TotalValue".desc)

val dfTop = df.withColumn("rn", row_number.over(w)).where($"rn" === 1).drop("rn")

dfTop.show
// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

This method will be inefficient in case of significant data skew.

Plain SQL aggregation followed by join:

Alternatively you can join with aggregated data frame:

val dfMax = df.groupBy($"hour".as("max_hour")).agg(max($"TotalValue").as("max_value"))

val dfTopByJoin = df.join(broadcast(dfMax),
    ($"hour" === $"max_hour") && ($"TotalValue" === $"max_value"))
  .drop("max_hour")
  .drop("max_value")

dfTopByJoin.show

// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

It will keep duplicate values (if there is more than one category per hour with the same total value). You can remove these as follows:

dfTopByJoin
  .groupBy($"hour")
  .agg(
    first("category").alias("category"),
    first("TotalValue").alias("TotalValue"))

Using ordering over structs:

Neat, although not very well tested, trick which doesn't require joins or window functions:

val dfTop = df.select($"Hour", struct($"TotalValue", $"Category").alias("vs"))
  .groupBy($"hour")
  .agg(max("vs").alias("vs"))
  .select($"Hour", $"vs.Category", $"vs.TotalValue")

dfTop.show
// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

With DataSet API (Spark 1.6+, 2.0+):

Spark 1.6:

case class Record(Hour: Integer, Category: String, TotalValue: Double)

df.as[Record]
  .groupBy($"hour")
  .reduce((x, y) => if (x.TotalValue > y.TotalValue) x else y)
  .show

// +---+--------------+
// | _1|            _2|
// +---+--------------+
// |[0]|[0,cat26,30.9]|
// |[1]|[1,cat67,28.5]|
// |[2]|[2,cat56,39.6]|
// |[3]| [3,cat8,35.6]|
// +---+--------------+

Spark 2.0 or later:

df.as[Record]
  .groupByKey(_.Hour)
  .reduceGroups((x, y) => if (x.TotalValue > y.TotalValue) x else y)

The last two methods can leverage map side combine and don't require full shuffle so most of the time should exhibit a better performance compared to window functions and joins. These cane be also used with Structured Streaming in completed output mode.

Don't use:

df.orderBy(...).groupBy(...).agg(first(...), ...)

It may seem to work (especially in the local mode) but it is unreliable (see SPARK-16207, credits to Tzach Zohar for linking relevant JIRA issue, and SPARK-30335).

The same note applies to

df.orderBy(...).dropDuplicates(...)

which internally uses equivalent execution plan.

There is little to be added to Garrett's great answer, but pandas also has a scatter method. Using that, it's as easy as

df = pd.DataFrame(np.random.randn(10,2), columns=['col1','col2'])
df['col3'] = np.arange(len(df))**2 * 100 + 100
df.plot.scatter('col1', 'col2', df['col3'])

Nobody mentioned it, but you can also simply use loc with the index and column labels.

df.loc[2, 'Letters']
# 'C'

Or, if you prefer to use "Numbers" column as reference, you can also set is as an index.

df.set_index('Numbers').loc[3, 'Letters']

This function will reorder your columns without losing data. Any omitted columns remain in the center of the data set:

def reorder_columns(columns, first_cols=[], last_cols=[], drop_cols=[]):
    columns = list(set(columns) - set(first_cols))
    columns = list(set(columns) - set(drop_cols))
    columns = list(set(columns) - set(last_cols))
    new_order = first_cols + columns + last_cols
    return new_order

Example usage:

my_list = ['first', 'second', 'third', 'fourth', 'fifth', 'sixth']
reorder_columns(my_list, first_cols=['fourth', 'third'], last_cols=['second'], drop_cols=['fifth'])

# Output:
['fourth', 'third', 'first', 'sixth', 'second']

To assign to your dataframe, use:

my_list = df.columns.tolist()
reordered_cols = reorder_columns(my_list, first_cols=['fourth', 'third'], last_cols=['second'], drop_cols=['fifth'])
df = df[reordered_cols]

Posting a java based solution, incase your team only uses java. The keyword inner will ensure that matching rows only are present in the final dataframe.

            Dataset<Row> joined = PersonDf.join(ProfileDf, 
                    PersonDf.col("personId").equalTo(ProfileDf.col("personId")),
                    "inner");
            joined.show();

To clarify one point in @EdChum's answer, per the documentation, you can include the object columns by using df.describe(include='all'). It won't provide many statistics, but will provide a few pieces of info, including count, number of unique values, top value. This may be a new feature, I don't know as I am a relatively new user.

Solution for pandas 0.24+ for converting numeric with missing values:

df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]})
print (df['column name'])
0    7500000.0
1    7500000.0
2          NaN
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)

ValueError: Cannot convert non-finite values (NA or inf) to integer

#http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
df['column name'] = df['column name'].astype('Int64')
print (df['column name'])
0    7500000
1    7500000
2        NaN
Name: column name, dtype: Int64

I think you need cast to numpy.int64:

df['column name'].astype(np.int64)

Sample:

df = pd.DataFrame({'column name':[7500000.0,7500000.0]})
print (df['column name'])
0    7500000.0
1    7500000.0
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)
#same as
#df['column name'] = df['column name'].astype(pd.np.int64)
print (df['column name'])
0    7500000
1    7500000
Name: column name, dtype: int64

If some NaNs in columns need replace them to some int (e.g. 0) by fillna, because type of NaN is float:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].fillna(0).astype(np.int64)
print (df['column name'])
0    7500000
1          0
Name: column name, dtype: int64

Also check documentation - missing data casting rules

EDIT:

Convert values with NaNs is buggy:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].values.astype(np.int64)
print (df['column name'])
0                7500000
1   -9223372036854775808
Name: column name, dtype: int64

You can use 'apply' to run a function or the rows or columns of a matrix or numerical data frame:

cluster1 <- data.frame(a=1:5, b=11:15, c=21:25, d=31:35)

apply(cluster1,2,mean)  # applies function 'mean' to 2nd dimension (columns)

apply(cluster1,1,mean)  # applies function to 1st dimension (rows)

sapply(cluster1, mean)  # also takes mean of columns, treating data frame like list of vectors

Understanding how to access multi-indexed pandas DataFrame can help you with all kinds of task like that.

Copy paste this in your code to generate example:

# hierarchical indices and columns
index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],
                                   names=['year', 'visit'])
columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],
                                     names=['subject', 'type'])

# mock some data
data = np.round(np.random.randn(4, 6), 1)
data[:, ::2] *= 10
data += 37

# create the DataFrame
health_data = pd.DataFrame(data, index=index, columns=columns)
health_data

Will give you table like this:

Standard access by column

health_data['Bob']
type       HR   Temp
year visit      
2013    1   22.0    38.6
        2   52.0    38.3
2014    1   30.0    38.9
        2   31.0    37.3


health_data['Bob']['HR']
year  visit
2013  1        22.0
      2        52.0
2014  1        30.0
      2        31.0
Name: HR, dtype: float64

# filtering by column/subcolumn - your case:
health_data['Bob']['HR']==22
year  visit
2013  1         True
      2        False
2014  1        False
      2        False

health_data['Bob']['HR'][2013]    
visit
1    22.0
2    52.0
Name: HR, dtype: float64

health_data['Bob']['HR'][2013][1]
22.0

Access by row

health_data.loc[2013]
subject Bob Guido   Sue
type    HR  Temp    HR  Temp    HR  Temp
visit                       
1   22.0    38.6    40.0    38.9    53.0    37.5
2   52.0    38.3    42.0    34.6    30.0    37.7

health_data.loc[2013,1] 
subject  type
Bob      HR      22.0
         Temp    38.6
Guido    HR      40.0
         Temp    38.9
Sue      HR      53.0
         Temp    37.5
Name: (2013, 1), dtype: float64

health_data.loc[2013,1]['Bob']
type
HR      22.0
Temp    38.6
Name: (2013, 1), dtype: float64

health_data.loc[2013,1]['Bob']['HR']
22.0

Slicing multi-index

idx=pd.IndexSlice
health_data.loc[idx[:,1], idx[:,'HR']]
    subject Bob Guido   Sue
type    HR  HR  HR
year    visit           
2013    1   22.0    40.0    53.0
2014    1   30.0    52.0    45.0

You can iterate over the index values if your dataframe has already been created.

df = df.groupby('l_customer_id_i').agg(lambda x: ','.join(x))
for name in df.index:
    print name
    print df.loc[name]

In PySpark 1.3 sort method doesn't take ascending parameter. You can use desc method instead:

from pyspark.sql.functions import col

(group_by_dataframe
    .count()
    .filter("`count` >= 10")
    .sort(col("count").desc()))

or desc function:

from pyspark.sql.functions import desc

(group_by_dataframe
    .count()
    .filter("`count` >= 10")
    .sort(desc("count"))

Both methods can be used with with Spark >= 1.3 (including Spark 2.x).

There is a built in method which would be the fastest method also, calling tolist on the .values np array:

df.values.tolist()

[[0.0, 3.61, 380.0, 3.0],
 [1.0, 3.67, 660.0, 3.0],
 [1.0, 3.19, 640.0, 4.0],
 [0.0, 2.93, 520.0, 4.0]]

I think the best way to do this with basic R is:

for( i in rownames(df) )
   print(df[i, "column1"])

The advantage over the for( i in 1:nrow(df))-approach is that you do not get into trouble if df is empty and nrow(df)=0.

I build this solution, reformulate does not take care if variable names have white spaces.

add_backticks = function(x) {
    paste0("`", x, "`")
}

x_lm_formula = function(x) {
    paste(add_backticks(x), collapse = " + ")
}

build_lm_formula = function(x, y){
    if (length(y)>1){
        stop("y needs to be just one variable")
    }
    as.formula(        
        paste0("`",y,"`", " ~ ", x_lm_formula(x))
    )
}

# Example
df <- data.frame(
    y = c(1,4,6), 
    x1 = c(4,-1,3), 
    x2 = c(3,9,8), 
    x3 = c(4,-4,-2)
    )

# Model Specification
columns = colnames(df)
y_cols = columns[1]
x_cols = columns[2:length(columns)]
formula = build_lm_formula(x_cols, y_cols)
formula
# output
# "`y` ~ `x1` + `x2` + `x3`"

# Run Model
lm(formula = formula, data = df)
# output
Call:
    lm(formula = formula, data = df)

Coefficients:
    (Intercept)           x1           x2           x3  
        -5.6316       0.7895       1.1579           NA  

```

FOMO:

The following example shows apply and applymap applied to a DataFrame.

map function is something you do apply on Series only. You cannot apply map on DataFrame.

The thing to remember is that apply can do anything applymap can, but apply has eXtra options.

The X factor options are: axis and result_type where result_type only works when axis=1 (for columns).

df = DataFrame(1, columns=list('abc'),
                  index=list('1234'))
print(df)

f = lambda x: np.log(x)
print(df.applymap(f)) # apply to the whole dataframe
print(np.log(df)) # applied to the whole dataframe
print(df.applymap(np.sum)) # reducing can be applied for rows only

# apply can take different options (vs. applymap cannot)
print(df.apply(f)) # same as applymap
print(df.apply(sum, axis=1))  # reducing example
print(df.apply(np.log, axis=1)) # cannot reduce
print(df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')) # expand result

As a sidenote, Series map function, should not be confused with the Python map function.

The first one is applied on Series, to map the values, and the second one to every item of an iterable.

Lastly don't confuse the dataframe apply method with groupby apply method.

If you have a very limited number of levels, you could try converting y into factor and change its levels.

> xy <- data.frame(x = c(1, 2, 4), y = c(1, 4, 5))
> xy$w <- as.factor(xy$y)
> levels(xy$w) <- c("good", "fair", "bad")
> xy
  x y    w
1 1 1 good
2 2 4 fair
3 4 5  bad

Some handy script:

hour = df['assess_time'].dt.hour.values[0]

Regards to your question... counting one Field? I decided to make it a question, but I hope it helps...

Say I have the following DataFrame

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.normal(0, 1, (5, 2)), columns=["A", "B"])

You could count a single column by

df.A.count()
#or
df['A'].count()

both evaluate to 5.

The cool thing (or one of many w.r.t. pandas) is that if you have NA values, count takes that into consideration.

So if I did

df['A'][1::2] = np.NAN
df.count()

The result would be

 A    3
 B    5

Previous answer is not correct in my experience, you can't pass it a simple string, needs to be a datetime object. So:

import datetime 
df.loc[datetime.date(year=2014,month=1,day=1):datetime.date(year=2014,month=2,day=1)]

This line will allow you to see all rows (up to the number that you set as 'max_rows') without any rows being hidden by the dots ('.....') that normally appear between head and tail in the print output.

pd.options.display.max_rows = 500

An alternative method to finding out the amount of rows in a dataframe which I think is the most readable variant is pandas.Index.size.

Do note that, as I commented on the accepted answer,

Suspected pandas.Index.size would actually be faster than len(df.index) but timeit on my computer tells me otherwise (~150 ns slower per loop).

Try the plyr package:

rbind.fill(a,b,c)

sort() was deprecated for DataFrames in favor of either:

• sort_values() to sort by column(s)
• sort_index() to sort by the index

sort() was deprecated (but still available) in Pandas with release 0.17 (2015-10-09) with the introduction of sort_values() and sort_index(). It was removed from Pandas with release 0.20 (2017-05-05).

A interesting question! my answer as below:

import pandas as pd

def sublst(row):
    return lst[row['J1']:row['J2']]

df = pd.DataFrame({'ID':['1','2','3'], 'J1': [0,2,3], 'J2':[1,4,5]})
print df
lst = ['a','b','c','d','e','f']

df['J3'] = df.apply(sublst,axis=1)
print df

Output:

  ID  J1  J2
0  1   0   1
1  2   2   4
2  3   3   5
  ID  J1  J2      J3
0  1   0   1     [a]
1  2   2   4  [c, d]
2  3   3   5  [d, e]

I changed the column name to ID,J1,J2,J3 to ensure ID < J1 < J2 < J3, so the column display in right sequence.

One more brief version:

import pandas as pd

df = pd.DataFrame({'ID':['1','2','3'], 'J1': [0,2,3], 'J2':[1,4,5]})
print df
lst = ['a','b','c','d','e','f']

df['J3'] = df.apply(lambda row:lst[row['J1']:row['J2']],axis=1)
print df

Vectors and matrices can only be of a single type and cbind and rbind on vectors will give matrices. In these cases, the numeric values will be promoted to character values since that type will hold all the values.

(Note that in your rbind example, the promotion happens within the c call:

> c(10, "[]", "[[1,2]]")
[1] "10"      "[]"      "[[1,2]]"

If you want a rectangular structure where the columns can be different types, you want a data.frame. Any of the following should get you what you want:

> x = data.frame(v1=c(10, 20), v2=c("[]", "[]"), v3=c("[[1,2]]","[[1,3]]"))
> x
  v1 v2      v3
1 10 [] [[1,2]]
2 20 [] [[1,3]]
> str(x)
'data.frame':   2 obs. of  3 variables:
 $ v1: num  10 20
 $ v2: Factor w/ 1 level "[]": 1 1
 $ v3: Factor w/ 2 levels "[[1,2]]","[[1,3]]": 1 2

or (using specifically the data.frame version of cbind)

> x = cbind.data.frame(c(10, 20), c("[]", "[]"), c("[[1,2]]","[[1,3]]"))
> x
  c(10, 20) c("[]", "[]") c("[[1,2]]", "[[1,3]]")
1        10            []                 [[1,2]]
2        20            []                 [[1,3]]
> str(x)
'data.frame':   2 obs. of  3 variables:
 $ c(10, 20)              : num  10 20
 $ c("[]", "[]")          : Factor w/ 1 level "[]": 1 1
 $ c("[[1,2]]", "[[1,3]]"): Factor w/ 2 levels "[[1,2]]","[[1,3]]": 1 2

or (using cbind, but making the first a data.frame so that it combines as data.frames do):

> x = cbind(data.frame(c(10, 20)), c("[]", "[]"), c("[[1,2]]","[[1,3]]"))
> x
  c.10..20. c("[]", "[]") c("[[1,2]]", "[[1,3]]")
1        10            []                 [[1,2]]
2        20            []                 [[1,3]]
> str(x)
'data.frame':   2 obs. of  3 variables:
 $ c.10..20.              : num  10 20
 $ c("[]", "[]")          : Factor w/ 1 level "[]": 1 1
 $ c("[[1,2]]", "[[1,3]]"): Factor w/ 2 levels "[[1,2]]","[[1,3]]": 1 2

Using dplyr::mutate:

library(dplyr)
df <- mutate(df, x = paste(n, s)) 

df 
> df
  n  s     b    x
1 2 aa  TRUE 2 aa
2 3 bb FALSE 3 bb
3 5 cc  TRUE 5 cc

• To remove white spaces:

1) To remove white space everywhere:

df.columns = df.columns.str.replace(' ', '')

2) To remove white space at the beginning of string:

df.columns = df.columns.str.lstrip()

3) To remove white space at the end of string:

df.columns = df.columns.str.rstrip()

4) To remove white space at both ends:

df.columns = df.columns.str.strip()

• To replace white spaces with other characters (underscore for instance):

5) To replace white space everywhere

df.columns = df.columns.str.replace(' ', '_')

6) To replace white space at the beginning:

df.columns = df.columns.str.replace('^ +', '_')

7) To replace white space at the end:

df.columns = df.columns.str.replace(' +$', '_')

8) To replace white space at both ends:

df.columns = df.columns.str.replace('^ +| +$', '_')

All above applies to a specific column as well, assume you have a column named col, then just do:

df[col] = df[col].str.strip()  # or .replace as above

Often times I think it is just good practice to keep larger databases inside a database (e.g. Postgres). I don't use anything too much larger than (nrow * ncol) ncell = 10M, which is pretty small; but I often find I want R to create and hold memory intensive graphs only while I query from multiple databases. In the future of 32 GB laptops, some of these types of memory problems will disappear. But the allure of using a database to hold the data and then using R's memory for the resulting query results and graphs still may be useful. Some advantages are:

(1) The data stays loaded in your database. You simply reconnect in pgadmin to the databases you want when you turn your laptop back on.

(2) It is true R can do many more nifty statistical and graphing operations than SQL. But I think SQL is better designed to query large amounts of data than R.

# Looking at Voter/Registrant Age by Decade

library(RPostgreSQL);library(lattice)

con <- dbConnect(PostgreSQL(), user= "postgres", password="password",
                 port="2345", host="localhost", dbname="WC2014_08_01_2014")

Decade_BD_1980_42 <- dbGetQuery(con,"Select PrecinctID,Count(PrecinctID),extract(DECADE from Birthdate) from voterdb where extract(DECADE from Birthdate)::numeric > 198 and PrecinctID in (Select * from LD42) Group By PrecinctID,date_part Order by Count DESC;")

Decade_RD_1980_42 <- dbGetQuery(con,"Select PrecinctID,Count(PrecinctID),extract(DECADE from RegistrationDate) from voterdb where extract(DECADE from RegistrationDate)::numeric > 198 and PrecinctID in (Select * from LD42) Group By PrecinctID,date_part Order by Count DESC;")

with(Decade_BD_1980_42,(barchart(~count | as.factor(precinctid))));
mtext("42LD Birthdays later than 1980 by Precinct",side=1,line=0)

with(Decade_RD_1980_42,(barchart(~count | as.factor(precinctid))));
mtext("42LD Registration Dates later than 1980 by Precinct",side=1,line=0)

I had a column(A) in a data frame with 3 values in it (yes, no, unknown). I wanted to filter only those rows which had a value "yes" for which this is the code, hope this will help you guys as well --

df <- df [(!(df$A=="no") & !(df$A=="unknown")),]

You can use pandas.cut:

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
   percentage     binned
0       46.50   (25, 50]
1       44.20   (25, 50]
2      100.00  (50, 100]
3       42.12   (25, 50]

bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
   percentage binned
0       46.50      5
1       44.20      5
2      100.00      6
3       42.12      5

Or numpy.searchsorted:

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
   percentage  binned
0       46.50       5
1       44.20       5
2      100.00       6
3       42.12       5

...and then value_counts or groupby and aggregate size:

s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50]     3
(50, 100]    1
(10, 25]     0
(5, 10]      0
(1, 5]       0
(0, 1]       0
Name: percentage, dtype: int64

s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1]       0
(1, 5]       0
(5, 10]      0
(10, 25]     0
(25, 50]     3
(50, 100]    1
dtype: int64

By default cut return categorical.

Series methods like Series.value_counts() will use all categories, even if some categories are not present in the data, operations in categorical.

Here are a couple dplyr options:

library(dplyr)

# all columns:
df %>% 
  mutate_all(~na_if(., ''))

# specific column types:
df %>% 
  mutate_if(is.factor, ~na_if(., ''))

# specific columns:  
df %>% 
  mutate_at(vars(A, B), ~na_if(., ''))

# or:
df %>% 
  mutate(A = replace(A, A == '', NA))

# replace can be used if you want something other than NA:
df %>% 
  mutate(A = as.character(A)) %>% 
  mutate(A = replace(A, A == '', 'used to be empty'))

Similar to unutbu above, you could also use applymap as follows:

import pandas as pd
df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
                  index=['foo','bar','baz','quux'],
                  columns=['cost'])

df = df.applymap("${0:.2f}".format)

Sure, you can use .get_loc():

In [45]: df = DataFrame({"pear": [1,2,3], "apple": [2,3,4], "orange": [3,4,5]})

In [46]: df.columns
Out[46]: Index([apple, orange, pear], dtype=object)

In [47]: df.columns.get_loc("pear")
Out[47]: 2

although to be honest I don't often need this myself. Usually access by name does what I want it to (df["pear"], df[["apple", "orange"]], or maybe df.columns.isin(["orange", "pear"])), although I can definitely see cases where you'd want the index number.

In general the point of the SettingWithCopyWarning is to show users (and especially new users) that they may be operating on a copy and not the original as they think. There are false positives (IOW if you know what you are doing it could be ok). One possibility is simply to turn off the (by default warn) warning as @Garrett suggest.

Here is another option:

In [1]: df = DataFrame(np.random.randn(5, 2), columns=list('AB'))

In [2]: dfa = df.ix[:, [1, 0]]

In [3]: dfa.is_copy
Out[3]: True

In [4]: dfa['A'] /= 2
/usr/local/bin/ipython:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
  #!/usr/local/bin/python

You can set the is_copy flag to False, which will effectively turn off the check, for that object:

In [5]: dfa.is_copy = False

In [6]: dfa['A'] /= 2

If you explicitly copy then no further warning will happen:

In [7]: dfa = df.ix[:, [1, 0]].copy()

In [8]: dfa['A'] /= 2

The code the OP is showing above, while legitimate, and probably something I do as well, is technically a case for this warning, and not a false positive. Another way to not have the warning would be to do the selection operation via reindex, e.g.

quote_df = quote_df.reindex(columns=['STK', ...])

Or,

quote_df = quote_df.reindex(['STK', ...], axis=1)  # v.0.21

No one seems to have included the which function. It can also prove useful for filtering.

expr[which(expr$cell == 'hesc'),]

This will also handle NAs and drop them from the resulting dataframe.

Running this on a 9840 by 24 dataframe 50000 times, it seems like the which method has a 60% faster run time than the %in% method.

where is probably what you're looking for. So

data=data.where(data=='-', None) 

From the panda docs:

where [returns] an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other).

functools.reduce and pd.concat are good solutions but in term of execution time pd.concat is the best.

from functools import reduce
import pandas as pd

dfs = [df1, df2, df3, ...]
nan_value = 0

# solution 1 (fast)
result_1 = pd.concat(dfs, join='outer', axis=1).fillna(nan_value)

# solution 2
result_2 = reduce(lambda df_left,df_right: pd.merge(df_left, df_right, 
                                              left_index=True, right_index=True, 
                                              how='outer'), 
                  dfs).fillna(nan_value)

First, here's some sample data:

set.seed(1)
dat <- data.frame(one = rnorm(15),
                 two = sample(LETTERS, 15),
                 three = rnorm(15),
                 four = runif(15))
dat <- data.frame(lapply(dat, function(x) { x[sample(15, 5)] <- NA; x }))
head(dat)
#          one  two       three      four
# 1         NA    M  0.80418951 0.8921983
# 2  0.1836433    O -0.05710677        NA
# 3 -0.8356286    L  0.50360797 0.3899895
# 4         NA    E          NA        NA
# 5  0.3295078    S          NA 0.9606180
# 6 -0.8204684 <NA> -1.28459935 0.4346595

Here's our replacement:

dat[["four"]][is.na(dat[["four"]])] <- 0
head(dat)
#          one  two       three      four
# 1         NA    M  0.80418951 0.8921983
# 2  0.1836433    O -0.05710677 0.0000000
# 3 -0.8356286    L  0.50360797 0.3899895
# 4         NA    E          NA 0.0000000
# 5  0.3295078    S          NA 0.9606180
# 6 -0.8204684 <NA> -1.28459935 0.4346595

Alternatively, you can, of course, write dat$four[is.na(dat$four)] <- 0

So I used to use a for loop for iterating through the dictionary as well, but one thing I've found that works much faster is to convert to a panel and then to a dataframe. Say you have a dictionary d

import pandas as pd
d
{'RAY Index': {datetime.date(2014, 11, 3): {'PX_LAST': 1199.46,
'PX_OPEN': 1200.14},
datetime.date(2014, 11, 4): {'PX_LAST': 1195.323, 'PX_OPEN': 1197.69},
datetime.date(2014, 11, 5): {'PX_LAST': 1200.936, 'PX_OPEN': 1195.32},
datetime.date(2014, 11, 6): {'PX_LAST': 1206.061, 'PX_OPEN': 1200.62}},
'SPX Index': {datetime.date(2014, 11, 3): {'PX_LAST': 2017.81,
'PX_OPEN': 2018.21},
datetime.date(2014, 11, 4): {'PX_LAST': 2012.1, 'PX_OPEN': 2015.81},
datetime.date(2014, 11, 5): {'PX_LAST': 2023.57, 'PX_OPEN': 2015.29},
datetime.date(2014, 11, 6): {'PX_LAST': 2031.21, 'PX_OPEN': 2023.33}}}

The command

pd.Panel(d)
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 4 (minor_axis)
Items axis: RAY Index to SPX Index
Major_axis axis: PX_LAST to PX_OPEN
Minor_axis axis: 2014-11-03 to 2014-11-06

where pd.Panel(d)[item] yields a dataframe

pd.Panel(d)['SPX Index']
2014-11-03  2014-11-04  2014-11-05 2014-11-06
PX_LAST 2017.81 2012.10 2023.57 2031.21
PX_OPEN 2018.21 2015.81 2015.29 2023.33

You can then hit the command to_frame() to turn it into a dataframe. I use reset_index as well to turn the major and minor axis into columns rather than have them as indices.

pd.Panel(d).to_frame().reset_index()
major   minor      RAY Index    SPX Index
PX_LAST 2014-11-03  1199.460    2017.81
PX_LAST 2014-11-04  1195.323    2012.10
PX_LAST 2014-11-05  1200.936    2023.57
PX_LAST 2014-11-06  1206.061    2031.21
PX_OPEN 2014-11-03  1200.140    2018.21
PX_OPEN 2014-11-04  1197.690    2015.81
PX_OPEN 2014-11-05  1195.320    2015.29
PX_OPEN 2014-11-06  1200.620    2023.33

Finally, if you don't like the way the frame looks you can use the transpose function of panel to change the appearance before calling to_frame() see documentation here http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Panel.transpose.html

Just as an example

pd.Panel(d).transpose(2,0,1).to_frame().reset_index()
major        minor  2014-11-03  2014-11-04  2014-11-05  2014-11-06
RAY Index   PX_LAST 1199.46    1195.323     1200.936    1206.061
RAY Index   PX_OPEN 1200.14    1197.690     1195.320    1200.620
SPX Index   PX_LAST 2017.81    2012.100     2023.570    2031.210
SPX Index   PX_OPEN 2018.21    2015.810     2015.290    2023.330

Hope this helps.

I believe that join() is just a convenience method. Try df1.merge(df2) instead, which allows you to specify left_on and right_on:

In [30]: left.merge(right, left_on="key1", right_on="key2")
Out[30]: 
  key1  lval key2  rval
0  foo     1  foo     4
1  bar     2  bar     5

If you are interested in only selecting one column this will work.

df[["item1"]].to_dict("records")

The below will NOT work and produces a TypeError: unsupported type: . I believe this is because it is trying to convert a series to a dict and not a Data Frame to a dict.

df["item1"].to_dict("records")

I had a requirement to only select one column and convert it to a list of dicts with the column name as the key and was stuck on this for a bit so figured I'd share.

A more general way of achieving column type transformation is as follows:

If you want to transform all your factor columns to character columns, e.g., this can be done using one pipe:

df %>%  mutate_each_( funs(as.character(.)), names( .[,sapply(., is.factor)] ))

You can use any:

print any(df.column == 07311954)
True       #true if it contains the number, false otherwise

If you rather want to see how many times '07311954' occurs in a column you can use:

df.column[df.column == 07311954].count()

Added streaming support based on the answer of @dunes:

import re
from json import JSONDecoder, JSONDecodeError

NOT_WHITESPACE = re.compile(r"[^\s]")


def stream_json(file_obj, buf_size=1024, decoder=JSONDecoder()):
    buf = ""
    ex = None
    while True:
        block = file_obj.read(buf_size)
        if not block:
            break
        buf += block
        pos = 0
        while True:
            match = NOT_WHITESPACE.search(buf, pos)
            if not match:
                break
            pos = match.start()
            try:
                obj, pos = decoder.raw_decode(buf, pos)
            except JSONDecodeError as e:
                ex = e
                break
            else:
                ex = None
                yield obj
        buf = buf[pos:]
    if ex is not None:
        raise ex

Using subset:

missing<-subset(a1, !(a %in% a2$a))

Pandas has the itertuples method to do exactly this:

list(df[['lat', 'long']].itertuples(index=False, name=None))

In Python 3.6 the fastest way is still the WouterOvermeire one. Kikohs' proposal is slower than the other two options.

import timeit

setup = '''
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(32, 120, 100000).reshape(50000,2),columns=list('AB'))
df['A'] = df['A'].apply(chr)
'''

timeit.Timer('dict(zip(df.A,df.B))', setup=setup).repeat(7,500)
timeit.Timer('pd.Series(df.A.values,index=df.B).to_dict()', setup=setup).repeat(7,500)
timeit.Timer('df.set_index("A").to_dict()["B"]', setup=setup).repeat(7,500)

Results:

1.1214002349999777 s  # WouterOvermeire
1.1922008498571748 s  # Jeff
1.7034366211428602 s  # Kikohs

You can transpose the single-row dataframe (which still results in a dataframe) and then squeeze the results into a series (the inverse of to_frame).

df = pd.DataFrame([list(range(5))], columns=["a{}".format(i) for i in range(5)])

>>> df.T.squeeze()  # Or more simply, df.squeeze() for a single row dataframe.
a0    0
a1    1
a2    2
a3    3
a4    4
Name: 0, dtype: int64

Note: To accommodate the point raised by @IanS (even though it is not in the OP's question), test for the dataframe's size. I am assuming that df is a dataframe, but the edge cases are an empty dataframe, a dataframe of shape (1, 1), and a dataframe with more than one row in which case the use should implement their desired functionality.

if df.empty:
    # Empty dataframe, so convert to empty Series.
    result = pd.Series()
elif df.shape == (1, 1)
    # DataFrame with one value, so convert to series with appropriate index.
    result = pd.Series(df.iat[0, 0], index=df.columns)
elif len(df) == 1:
    # Convert to series per OP's question.
    result = df.T.squeeze()
else:
    # Dataframe with multiple rows.  Implement desired behavior.
    pass

This can also be simplified along the lines of the answer provided by @themachinist.

if len(df) > 1:
    # Dataframe with multiple rows.  Implement desired behavior.
    pass
else:
    result = pd.Series() if df.empty else df.iloc[0, :]

One simple solution:

cond1 <- df$sub == 1 & df$day == 2

cond2 <- df$sub == 3 & df$day == 4

df <- df[!(cond1 | cond2),]

"all" option does not work anymore, The new parameter is;

x = pd.merge(df1, df2, how="outer")

DataFrame.reset_index is what you're looking for. If you don't want it saved as a column, then do:

df = df.reset_index(drop=True)

If you don't want to reassign:

df.reset_index(drop=True, inplace=True)

You can try as.vector(t(test)). Please note that, if you want to do it by columns you should use unlist(test).

The error comes up when you are trying to assign a list of numpy array of different length to a data frame, and it can be reproduced as follows:

A data frame of four rows:

df = pd.DataFrame({'A': [1,2,3,4]})

Now trying to assign a list/array of two elements to it:

df['B'] = [3,4]   # or df['B'] = np.array([3,4])

Both errors out:

ValueError: Length of values does not match length of index

Because the data frame has four rows but the list and array has only two elements.

Work around Solution (use with caution): convert the list/array to a pandas Series, and then when you do assignment, missing index in the Series will be filled with NaN:

df['B'] = pd.Series([3,4])

df
#   A     B
#0  1   3.0
#1  2   4.0
#2  3   NaN          # NaN because the value at index 2 and 3 doesn't exist in the Series
#3  4   NaN

For your specific problem, if you don't care about the index or the correspondence of values between columns, you can reset index for each column after dropping the duplicates:

df.apply(lambda col: col.drop_duplicates().reset_index(drop=True))

#   A     B
#0  1   1.0
#1  2   5.0
#2  7   9.0
#3  8   NaN

The below works for me

dataframe[,"newName"] <- NA

Make sure to add "" for new name string.

More flexibility using .query with Pandas >= 0.25.0:

August 2019 updated answer

Since Pandas >= 0.25.0 we can use the query method to filter dataframes with Pandas methods and even column names which have spaces. Normally the spaces in column names would give an error, but now we can solve that using a backtick (`) - see GitHub:

# Example dataframe
df = pd.DataFrame({'Sender email':['[email protected]', "[email protected]", "[email protected]"]})

     Sender email
0  [email protected]
1  [email protected]
2    [email protected]

Using .query with method str.endswith:

df.query('`Sender email`.str.endswith("@shop.com")')

Output

     Sender email
1  [email protected]
2    [email protected]

Also we can use local variables by prefixing it with an @ in our query:

domain = 'shop.com'
df.query('`Sender email`.str.endswith(@domain)')

Output

     Sender email
1  [email protected]
2    [email protected]

Thanks Notable1, works for me with the tidytextr Create a dataframe with the name of files in one column and content in other.

    diretorio <- "D:/base"
    arquivos <- list.files(diretorio, pattern = "*.PDF")
    quantidade <- length(arquivos)

#
df = NULL
for (k in 1:quantidade) {

      nome = arquivos[k]
      print(nome)
      Sys.sleep(1) 
      dados = read_pdf(arquivos[k],ocr = T)
      print(dados)
      Sys.sleep(1)
      df = rbind(df, data.frame(nome,dados))
      Sys.sleep(1)
}
Encoding(df$text) <- "UTF-8"

Try this one:

json.dumps(json.loads(df.to_json(orient="records")))

You can just use the column name directly:

df <- data.frame(A=1:10, B=2:11, C=3:12)
fun1 <- function(x, column){
  max(x[,column])
}
fun1(df, "B")
fun1(df, c("B","A"))

There's no need to use substitute, eval, etc.

You can even pass the desired function as a parameter:

fun1 <- function(x, column, fn) {
  fn(x[,column])
}
fun1(df, "B", max)

Alternatively, using [[ also works for selecting a single column at a time:

df <- data.frame(A=1:10, B=2:11, C=3:12)
fun1 <- function(x, column){
  max(x[[column]])
}
fun1(df, "B")

print df.sort_index(by='Frequency',ascending=False)

where by is the name of the column,if you want to sort the dataset based on column

using the top answer produces a warning about making changes to a copy of a df slice. Assuming that you have other columns, a better way to do this is to pass a dictionary:
df.fillna({'A': 'NA', 'B': 'NA'}, inplace=True)

If you want to set the column you filter on as a new index, you could also consider to use .filter; if you want to keep it as a separate column then str.contains is the way to go.

Let's say you have

df = pd.DataFrame({'vals': [1, 2, 3, 4, 5], 'ids': [u'aball', u'bball', u'cnut', u'fball', 'ballxyz']})

       ids  vals
0    aball     1
1    bball     2
2     cnut     3
3    fball     4
4  ballxyz     5

and your plan is to filter all rows in which ids contains ball AND set ids as new index, you can do

df.set_index('ids').filter(like='ball', axis=0)

which gives

         vals
ids          
aball       1
bball       2
fball       4
ballxyz     5

But filter also allows you to pass a regex, so you could also filter only those rows where the column entry ends with ball. In this case you use

df.set_index('ids').filter(regex='ball$', axis=0)

       vals
ids        
aball     1
bball     2
fball     4

Note that now the entry with ballxyz is not included as it starts with ball and does not end with it.

If you want to get all entries that start with ball you can simple use

df.set_index('ids').filter(regex='^ball', axis=0)

yielding

         vals
ids          
ballxyz     5

The same works with columns; all you then need to change is the axis=0 part. If you filter based on columns, it would be axis=1.

Here are several options for getting the "tail" items of an iterable:

Given

n = 9
iterable = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Desired Output

[2, 3, 4, 5, 6, 7, 8, 9, 10]

Code

We get the latter output using any of the following options:

from collections import deque
import itertools

import more_itertools


# A: Slicing
iterable[-n:]


# B: Implement an itertools recipe
def tail(n, iterable):
    """Return an iterator over the last *n* items of *iterable*.

        >>> t = tail(3, 'ABCDEFG')
        >>> list(t)
        ['E', 'F', 'G']

    """
    return iter(deque(iterable, maxlen=n))
list(tail(n, iterable))


# C: Use an implemented recipe, via more_itertools
list(more_itertools.tail(n, iterable))


# D: islice, via itertools
list(itertools.islice(iterable, len(iterable)-n, None))


# E: Negative islice, via more_itertools
list(more_itertools.islice_extended(iterable, -n, None))

Details

• A. Traditional Python slicing is inherent to the language. This option works with sequences such as strings, lists and tuples. However, this kind of slicing does not work on iterators, e.g. iter(iterable).
• B. An itertools recipe. It is generalized to work on any iterable and resolves the iterator issue in the last solution. This recipe must be implemented manually as it is not officially included in the itertools module.
• C. Many recipes, including the latter tool (B), have been conveniently implemented in third party packages. Installing and importing these these libraries obviates manual implementation. One of these libraries is called more_itertools (install via > pip install more-itertools); see more_itertools.tail.
• D. A member of the itertools library. Note, itertools.islice does not support negative slicing.
• E. Another tool is implemented in more_itertools that generalizes itertools.islice to support negative slicing; see more_itertools.islice_extended.

Which one do I use?

It depends. In most cases, slicing (option A, as mentioned in other answers) is most simple option as it built into the language and supports most iterable types. For more general iterators, use any of the remaining options. Note, options C and E require installing a third-party library, which some users may find useful.

I had the same problem and this is how I solved it. I'm on rails 5.1.0rc1

Make sure to add require jquery and tether inside your application.js file they must be at the very top like this

//= require jquery
//= require tether

Just make sure to have tether installed

The top answer is equivalent to doing:

let text = find.reduce((acc, item, i) => {
  const regex = new RegExp(item, "g");
  return acc.replace(regex, replace[i]);
}, textarea);

Given this:

var textarea = $(this).val();
var find = ["<", ">", "\n"];
var replace = ["&lt;", "&gt;", "<br/>"];

In this case, no imperative programming is going on.

When i will getting this error on my site .it will stop some functionality on my site, after research i find the solution for this problem ,

$colorpicker_inputs.live('focus', function(e) {
    jQuery(this).next('.farb-popup').show();
    jQuery(this).parents('li').css( {
        position : 'relative',
        zIndex : '9999'
    })
    jQuery('#tabber').css( {
        overflow : 'visible'
    });
});

$colorpicker_inputs.live('blur', function(e) {
    jQuery(this).next('.farb-popup').hide();
    jQuery(this).parents('li').css( {
        zIndex : '0'
    })
});

Should be replace 'live' to 'on' check below

    $colorpicker_inputs.on('focus', function(e) {
    jQuery(this).next('.farb-popup').show();
    jQuery(this).parents('li').css( {
        position : 'relative',
        zIndex : '9999'
    })
    jQuery('#tabber').css( {
        overflow : 'visible'
    });
});

$colorpicker_inputs.on('blur', function(e) {
    jQuery(this).next('.farb-popup').hide();
    jQuery(this).parents('li').css( {
        zIndex : '0'
    })
});

One more basic exmaple below :

.live(event, selector, function) 

Change it to :

.on(event, selector, function)

Like it's written up there, you forget to type #include <sstream>

#include <sstream>
using namespace std;

QString Stats_Manager::convertInt(int num)
{
   stringstream ss;
   ss << num;
   return ss.str();
}

You can also use some other ways to convert int to string, like

char numstr[21]; // enough to hold all numbers up to 64-bits
sprintf(numstr, "%d", age);
result = name + numstr;

check this!

if using logging.config.fileConfig with a configuration file use something like:

[formatter_simpleFormatter]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
datefmt=%Y-%m-%d %H:%M:%S

Use the btoa() function to encode:

console.log(btoa("password")); // cGFzc3dvcmQ=

To decode, you can use the atob() function:

console.log(atob("cGFzc3dvcmQ=")); // password

beware of the JavaScript specific definition of null. there are two definitions for "no value" in javascript. 1. Null: when a variable is null, it means it contains no data in it, but the variable is already defined in the code. like this:

var myEmptyValue = 1;
myEmptyValue = null;
if ( myEmptyValue === null ) { window.alert('it is null'); }
// alerts

in such case, the type of your variable is actually Object. test it.

window.alert(typeof myEmptyValue); // prints Object

2. Undefined: when a variable has not been defined before in the code, and as expected, it does not contain any value. like this:

   if ( myUndefinedValue === undefined ) { window.alert('it is undefined'); }
   // alerts
   

if such case, the type of your variable is 'undefined'.

notice that if you use the type-converting comparison operator (==), JavaScript will act equally for both of these empty-values. to distinguish between them, always use the type-strict comparison operator (===).

For those who remove app from sale, keep following in mind:

• In app purchase items will NOT be removed automatically, user still can buy them, you have to change each item status.
• Users who've downloaded the app before still can restore/redownload app.

See details: Removing and app from sale.

If you want to completely remove your App, you should delete your app.

This is my generic solution for any string s and any index i:

def remove_at(i, s):
    return s[:i] + s[i+1:]

my friend this the will fix ur problem ;)

in root of folder ( xampp ) just run this file ( setup_xampp.bat ) then press enter

and try to start the apache server

every things will work like charm ;)

$ dseditgroup -o edit -u <adminusername> -t user -a <developerusername> _developer

sudo chown -R $(whoami):admin /usr/local

That will give permissions back (Homebrew installs ruby there)

This problem is mainly in gradle or in misversioned libraries, including, from libraries, when both define the same class. Expand and check, imported external libraries...

You cannot have two same classes to be exported to one place, or code, therefore, dexer does not know which one should be used...

SHOW FULL PROCESSLIST

If you don't use FULL, "only the first 100 characters of each statement are shown in the Info field".

When using phpMyAdmin, you should also click on the "Full texts" option ("? T ?" on top left corner of a results table) to see untruncated results.

• Type ipconfig at the commmand prompt.
• It will give you your IPv4 Address
• Replace localhost with your IPv4 Address in the Url when hitting the service.

The traffic will show up in Fiddler under your computer's IP address.

This overloaded version of the save function works for me:

yourDF.save(outputPath, org.apache.spark.sql.SaveMode.valueOf("Overwrite"))

The example above would overwrite an existing folder. The savemode can take these parameters as well (https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/sql/SaveMode.html):

Append: Append mode means that when saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data.

ErrorIfExists: ErrorIfExists mode means that when saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.

Ignore: Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data.

AngularJS : AngularJS is for developing heavy web applications. AngularJS can use jQuery if it’s present in the web-app when the application is being bootstrapped. If it's not present in the script path, then AngularJS falls back to its own implementation of the subset of jQuery.

JQuery : jQuery is a small, fast, and feature-rich JavaScript library. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler. jQuery simplifies a lot of the complicated things from JavaScript, like AJAX calls and DOM manipulation.

Read more details here: angularjs-vs-jquery

If you have some params, you can do this.

    $results = DB::table('rooms')
    ->distinct()
    ->leftJoin('bookings', function($join) use ($param1, $param2)
    {
        $join->on('rooms.id', '=', 'bookings.room_type_id');
        $join->on('arrival','=',DB::raw("'".$param1."'"));
        $join->on('arrival','=',DB::raw("'".$param2."'"));

    })
    ->where('bookings.room_type_id', '=', NULL)
    ->get();

and then return your query

return $results;

Consider using matplotlib.cbook pieces

for example:

import matplotlib.cbook as cbook
segments = cbook.pieces(np.arange(20), 3)
for s in segments:
     print s

If you have a relatively- (or otherwise-) positioned div you can center something inside it with margin:auto

Vertical centering is a bit tricker, but possible.

• First create the menu layouts in the your Activity layout xml file. For e.g. a linear layout with horizontal orientation and include a TextView for label then a Floating Action Button beside the TextView.

• Create the menu layouts as per your need and number.

• Create a Base Floating Action Button and on its click of that change the visibility of the Menu Layouts.

Please check the below code for the reference and for more info checkout my project from github

Checkout project from Github

<android.support.constraint.ConstraintLayout
            android:id="@+id/activity_main"
            android:layout_width="match_parent"
            android:layout_height="match_parent"
            tools:context="com.app.fabmenu.MainActivity">

            <android.support.design.widget.FloatingActionButton
                android:id="@+id/baseFloatingActionButton"
                android:layout_width="wrap_content"
                android:layout_height="wrap_content"
                android:layout_marginBottom="16dp"
                android:layout_marginEnd="16dp"
                android:layout_marginRight="16dp"
                android:clickable="true"
                android:onClick="@{FabHandler::onBaseFabClick}"
                android:tint="@android:color/white"
                app:fabSize="normal"
                app:layout_constraintBottom_toBottomOf="@+id/activity_main"
                app:layout_constraintRight_toRightOf="@+id/activity_main"
                app:srcCompat="@drawable/ic_add_black_24dp" />

            <LinearLayout
                android:id="@+id/shareLayout"
                android:layout_width="wrap_content"
                android:layout_height="wrap_content"
                android:layout_marginBottom="12dp"
                android:layout_marginEnd="24dp"
                android:layout_marginRight="24dp"
                android:gravity="center_vertical"
                android:orientation="horizontal"
                android:visibility="invisible"
                app:layout_constraintBottom_toTopOf="@+id/createLayout"
                app:layout_constraintLeft_toLeftOf="@+id/createLayout"
                app:layout_constraintRight_toRightOf="@+id/activity_main">

                <TextView
                    android:id="@+id/shareLabelTextView"
                    android:layout_width="wrap_content"
                    android:layout_height="wrap_content"
                    android:layout_marginEnd="8dp"
                    android:layout_marginRight="8dp"
                    android:background="@drawable/shape_fab_label"
                    android:elevation="2dp"
                    android:fontFamily="sans-serif"
                    android:padding="5dip"
                    android:text="Share"
                    android:textColor="@android:color/white"
                    android:typeface="normal" />


                <android.support.design.widget.FloatingActionButton
                    android:id="@+id/shareFab"
                    android:layout_width="wrap_content"
                    android:layout_height="wrap_content"
                    android:clickable="true"
                    android:onClick="@{FabHandler::onShareFabClick}"
                    android:tint="@android:color/white"
                    app:fabSize="mini"
                    app:srcCompat="@drawable/ic_share_black_24dp" />

            </LinearLayout>

            <LinearLayout
                android:id="@+id/createLayout"
                android:layout_width="wrap_content"
                android:layout_height="wrap_content"
                android:layout_marginBottom="24dp"
                android:layout_marginEnd="24dp"
                android:layout_marginRight="24dp"
                android:gravity="center_vertical"
                android:orientation="horizontal"
                android:visibility="invisible"
                app:layout_constraintBottom_toTopOf="@+id/baseFloatingActionButton"
                app:layout_constraintRight_toRightOf="@+id/activity_main">

                <TextView
                    android:id="@+id/createLabelTextView"
                    android:layout_width="wrap_content"
                    android:layout_height="wrap_content"
                    android:layout_marginEnd="8dp"
                    android:layout_marginRight="8dp"
                    android:background="@drawable/shape_fab_label"
                    android:elevation="2dp"
                    android:fontFamily="sans-serif"
                    android:padding="5dip"
                    android:text="Create"
                    android:textColor="@android:color/white"
                    android:typeface="normal" />

                <android.support.design.widget.FloatingActionButton
                    android:id="@+id/createFab"
                    android:layout_width="wrap_content"
                    android:layout_height="wrap_content"
                    android:clickable="true"
                    android:onClick="@{FabHandler::onCreateFabClick}"
                    android:tint="@android:color/white"
                    app:fabSize="mini"
                    app:srcCompat="@drawable/ic_create_black_24dp" />

            </LinearLayout>

        </android.support.constraint.ConstraintLayout>

These are the animations-

Opening animation of FAB Menu:

<?xml version="1.0" encoding="utf-8"?>
<set xmlns:android="http://schemas.android.com/apk/res/android"
android:fillAfter="true">
<scale
    android:duration="300"
    android:fromXScale="0"
    android:fromYScale="0"
    android:interpolator="@android:anim/linear_interpolator"
    android:pivotX="50%"
    android:pivotY="50%"
    android:toXScale="1"
    android:toYScale="1" />
<alpha
    android:duration="300"
    android:fromAlpha="0.0"
    android:interpolator="@android:anim/accelerate_interpolator"
    android:toAlpha="1.0" />

</set>

Closing animation of FAB Menu:

<?xml version="1.0" encoding="utf-8"?>
<set xmlns:android="http://schemas.android.com/apk/res/android"
android:fillAfter="true">
<scale
    android:duration="300"
    android:fromXScale="1"
    android:fromYScale="1"
    android:interpolator="@android:anim/linear_interpolator"
    android:pivotX="50%"
    android:pivotY="50%"
    android:toXScale="0.0"
    android:toYScale="0.0" />
<alpha
    android:duration="300"
    android:fromAlpha="1.0"
    android:interpolator="@android:anim/accelerate_interpolator"
    android:toAlpha="0.0" />
</set>

Then in my Activity I've simply used the animations above to show and hide the FAB menu :

Show Fab Menu:

  private void expandFabMenu() {

    ViewCompat.animate(binding.baseFloatingActionButton).rotation(45.0F).withLayer().setDuration(300).setInterpolator(new OvershootInterpolator(10.0F)).start();
    binding.createLayout.startAnimation(fabOpenAnimation);
    binding.shareLayout.startAnimation(fabOpenAnimation);
    binding.createFab.setClickable(true);
    binding.shareFab.setClickable(true);
    isFabMenuOpen = true;

}

Close Fab Menu:

private void collapseFabMenu() {

    ViewCompat.animate(binding.baseFloatingActionButton).rotation(0.0F).withLayer().setDuration(300).setInterpolator(new OvershootInterpolator(10.0F)).start();
    binding.createLayout.startAnimation(fabCloseAnimation);
    binding.shareLayout.startAnimation(fabCloseAnimation);
    binding.createFab.setClickable(false);
    binding.shareFab.setClickable(false);
    isFabMenuOpen = false;

}

Here is the the Activity class -

package com.app.fabmenu;

import android.databinding.DataBindingUtil;
import android.os.Bundle;
import android.support.design.widget.Snackbar;
import android.support.v4.view.ViewCompat;
import android.support.v7.app.AppCompatActivity;
import android.view.View;
import android.view.animation.Animation;
import android.view.animation.AnimationUtils;
import android.view.animation.OvershootInterpolator;

import com.app.fabmenu.databinding.ActivityMainBinding;

public class MainActivity extends AppCompatActivity {

private ActivityMainBinding binding;
private Animation fabOpenAnimation;
private Animation fabCloseAnimation;
private boolean isFabMenuOpen = false;

@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_main);

    binding = DataBindingUtil.setContentView(this,    R.layout.activity_main);
    binding.setFabHandler(new FabHandler());

    getAnimations();


}

private void getAnimations() {

    fabOpenAnimation = AnimationUtils.loadAnimation(this, R.anim.fab_open);

    fabCloseAnimation = AnimationUtils.loadAnimation(this, R.anim.fab_close);

}

private void expandFabMenu() {

    ViewCompat.animate(binding.baseFloatingActionButton).rotation(45.0F).withLayer().setDuration(300).setInterpolator(new OvershootInterpolator(10.0F)).start();
    binding.createLayout.startAnimation(fabOpenAnimation);
    binding.shareLayout.startAnimation(fabOpenAnimation);
    binding.createFab.setClickable(true);
    binding.shareFab.setClickable(true);
    isFabMenuOpen = true;


}

private void collapseFabMenu() {

    ViewCompat.animate(binding.baseFloatingActionButton).rotation(0.0F).withLayer().setDuration(300).setInterpolator(new OvershootInterpolator(10.0F)).start();
    binding.createLayout.startAnimation(fabCloseAnimation);
    binding.shareLayout.startAnimation(fabCloseAnimation);
    binding.createFab.setClickable(false);
    binding.shareFab.setClickable(false);
    isFabMenuOpen = false;

}


public class FabHandler {

    public void onBaseFabClick(View view) {

        if (isFabMenuOpen)
            collapseFabMenu();
        else
            expandFabMenu();


    }

    public void onCreateFabClick(View view) {

        Snackbar.make(binding.coordinatorLayout, "Create FAB tapped", Snackbar.LENGTH_SHORT).show();

    }

    public void onShareFabClick(View view) {

        Snackbar.make(binding.coordinatorLayout, "Share FAB tapped", Snackbar.LENGTH_SHORT).show();

    }


}

@Override
public void onBackPressed() {

    if (isFabMenuOpen)
        collapseFabMenu();
    else
        super.onBackPressed();
}
}

Here are the screenshots

In short: if you are inputting a string array of length t, then Scanner#nextLine() expects t lines, each entry in the string array is differentiated from the other by enter key.And Scanner#next() will keep taking inputs till you press enter but stores string(word) inside the array, which is separated by whitespace.

Lets have a look at following snippet of code

    Scanner in = new Scanner(System.in);
    int t = in.nextInt();
    String[] s = new String[t];

    for (int i = 0; i < t; i++) {
        s[i] = in.next();
    }

when I run above snippet of code in my IDE (lets say for string length 2),it does not matter whether I enter my string as

Input as :- abcd abcd or

Input as :-

abcd

abcd

Output will be like abcd

abcd

But if in same code we replace next() method by nextLine()

    Scanner in = new Scanner(System.in);
    int t = in.nextInt();
    String[] s = new String[t];

    for (int i = 0; i < t; i++) {
        s[i] = in.nextLine();
    }

Then if you enter input on prompt as - abcd abcd

Output is :-

abcd abcd

and if you enter the input on prompt as abcd (and if you press enter to enter next abcd in another line, the input prompt will just exit and you will get the output)

Output is:-

abcd

A new favorite for me is @SuppressWarnings("WeakerAccess") in IntelliJ, which keeps it from complaining when it thinks you should have a weaker access modifier than you are using. We have to have public access for some methods to support testing, and the @VisibleForTesting annotation doesn't prevent the warnings.

ETA: "Anonymous" commented, on the page @MattCampbell linked to, the following incredibly useful note:

You shouldn't need to use this list for the purpose you are describing. IntelliJ will add those SuppressWarnings for you automatically if you ask it to. It has been capable of doing this for as many releases back as I remember.

Just go to the location where you have the warning and type Alt-Enter (or select it in the Inspections list if you are seeing it there). When the menu comes up, showing the warning and offering to fix it for you (e.g. if the warning is "Method may be static" then "make static" is IntellJ's offer to fix it for you), instead of selecting "enter", just use the right arrow button to access the submenu, which will have options like "Edit inspection profile setting" and so forth. At the bottom of this list will be options like "Suppress all inspections for class", "Suppress for class", "Suppress for method", and occasionally "Suppress for statement". You probably want whichever one of these appears last on the list. Selecting one of these will add a @SuppressWarnings annotation (or comment in some cases) to your code suppressing the warning in question. You won't need to guess at which annotation to add, because IntelliJ will choose based on the warning you selected.

Adding a solution that doesn't use WinForms but NativeMethods instead. First you need to define the native methods needed.

public static class NativeMethods
{
    public const Int32 MONITOR_DEFAULTTOPRIMERTY = 0x00000001;
    public const Int32 MONITOR_DEFAULTTONEAREST = 0x00000002;


    [DllImport( "user32.dll" )]
    public static extern IntPtr MonitorFromWindow( IntPtr handle, Int32 flags );


    [DllImport( "user32.dll" )]
    public static extern Boolean GetMonitorInfo( IntPtr hMonitor, NativeMonitorInfo lpmi );


    [Serializable, StructLayout( LayoutKind.Sequential )]
    public struct NativeRectangle
    {
        public Int32 Left;
        public Int32 Top;
        public Int32 Right;
        public Int32 Bottom;


        public NativeRectangle( Int32 left, Int32 top, Int32 right, Int32 bottom )
        {
            this.Left = left;
            this.Top = top;
            this.Right = right;
            this.Bottom = bottom;
        }
    }


    [StructLayout( LayoutKind.Sequential, CharSet = CharSet.Auto )]
    public sealed class NativeMonitorInfo
    {
        public Int32 Size = Marshal.SizeOf( typeof( NativeMonitorInfo ) );
        public NativeRectangle Monitor;
        public NativeRectangle Work;
        public Int32 Flags;
    }
}

And then get the monitor handle and the monitor info like this.

        var hwnd = new WindowInteropHelper( this ).EnsureHandle();
        var monitor = NativeMethods.MonitorFromWindow( hwnd, NativeMethods.MONITOR_DEFAULTTONEAREST );

        if ( monitor != IntPtr.Zero )
        {
            var monitorInfo = new NativeMonitorInfo();
            NativeMethods.GetMonitorInfo( monitor, monitorInfo );

            var left = monitorInfo.Monitor.Left;
            var top = monitorInfo.Monitor.Top;
            var width = ( monitorInfo.Monitor.Right - monitorInfo.Monitor.Left );
            var height = ( monitorInfo.Monitor.Bottom - monitorInfo.Monitor.Top );
        }

Had it been on Linux the problem would be that localhost is the loopback interface, you need to application to bind to your network interface.

You can use the netstat to confirm that it is not bound to the expected network interface.

You can make this work by invoking the program with the system parameter java.rmi.server.hostname="YOUR_IP", either as an environment variable or using

java -Djava.rmi.server.hostname=YOUR_IP YOUR_APP

Try use it:

Uri uri =  Uri.fromFile(entry);
Intent intent = new Intent(android.content.Intent.ACTION_VIEW);
String mime = "*/*";
MimeTypeMap mimeTypeMap = MimeTypeMap.getSingleton();
if (mimeTypeMap.hasExtension(
    mimeTypeMap.getFileExtensionFromUrl(uri.toString())))
    mime = mimeTypeMap.getMimeTypeFromExtension(
        mimeTypeMap.getFileExtensionFromUrl(uri.toString()));
intent.setDataAndType(uri,mime);
startActivity(intent);

Use:

SELECT *
  FROM YOUR_TABLE
 WHERE creation_date <= TRUNC(SYSDATE) - 30

SYSDATE returns the date & time; TRUNC resets the date to being as of midnight so you can omit it if you want the creation_date that is 30 days previous including the current time.

Depending on your needs, you could also look at using ADD_MONTHS:

SELECT *
  FROM YOUR_TABLE
 WHERE creation_date <= ADD_MONTHS(TRUNC(SYSDATE), -1)

As suggested above the inclusion of

/usr/lib/openmpi/include 

in the include path takes care of this (in my case)

Suppose I have a model User

1. User.find(id)

Returns a row where primary key = id. The return type will be User object.

2. User.find_by(email:"[email protected]")

Returns first row with matching attribute or email in this case. Return type will be User object again.

Note :- User.find_by(email: "[email protected]") is similar to User.find_by_email("[email protected]")

3. User.where(project_id:1)

Returns all users in users table where attribute matches.

Here return type will be ActiveRecord::Relation object. ActiveRecord::Relation class includes Ruby's Enumerable module so you can use it's object like an array and traverse on it.

CLOCK_REALTIME is affected by NTP, and can move forwards and backwards. CLOCK_MONOTONIC is not, and advances at one tick per tick.

I tried few options but, this works best for me:

String text = "<strike><font color=\'#757575\'>Some text</font></strike>";
textview.setText(Html.fromHtml(text));

cheers

You simply need to upgrade your Tomcat version, to Tomcat 8.0.xx. Java8 <-> Tomcat8

This is the configuration that I have been using and it has always worked out well

If I am to define the same proptypes for a particular shape multiple times, I like abstract it out to a proptypes file so that if the shape of the object changes, I only have to change the code in one place. It helps dry up the codebase a bit.

Example:

// Inside my proptypes.js file
import PT from 'prop-types';

export const product = {
  id: PT.number.isRequired,
  title: PT.string.isRequired,
  sku: PT.string.isRequired,
  description: PT.string.isRequired,
};


// Inside my component file
import PT from 'prop-types';
import { product } from './proptypes;


List.propTypes = {
  productList: PT.arrayOf(product)
}

Collections.sort(teamsName.subList(1, teamsName.size()));

The code above will reflect the actual sublist of your original list sorted.

Add the permissions to the app manifest

Add one of the following permissions as a child of the element in your Android manifest. Either the coarse location permission:

<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="com.example.myapp" >
  ...
  <uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION"/>
  ...
</manifest>

Or the fine location permission:

<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="com.example.myapp" >
  ...
  <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION"/>
  ...
</manifest>

The following code sample checks for permission using the Support library before enabling the My Location layer:

if (ContextCompat.checkSelfPermission(this, Manifest.permission.ACCESS_FINE_LOCATION)
                == PackageManager.PERMISSION_GRANTED) {
    mMap.setMyLocationEnabled(true);
} else {
    // Show rationale and request permission.
}
The following sample handles the result of the permission request by implementing the ActivityCompat.OnRequestPermissionsResultCallback from the Support library:

@Override
public void onRequestPermissionsResult(int requestCode, String[] permissions, int[] grantResults) {
    if (requestCode == MY_LOCATION_REQUEST_CODE) {
      if (permissions.length == 1 &&
          permissions[0] == Manifest.permission.ACCESS_FINE_LOCATION &&
          grantResults[0] == PackageManager.PERMISSION_GRANTED) {
        mMap.setMyLocationEnabled(true);
    } else {
      // Permission was denied. Display an error message.
    }
}

This example provides current location update using GPS provider. Entire Android app code is as follows,

import android.os.Bundle;
import android.app.Activity;
import android.content.Context;
import android.location.Location;
import android.location.LocationListener;
import android.location.LocationManager;
import android.widget.TextView;

import android.util.Log;

public class MainActivity extends Activity implements LocationListener{
protected LocationManager locationManager;
protected LocationListener locationListener;
protected Context context;
TextView txtLat;
String lat;
String provider;
protected String latitude,longitude; 
protected boolean gps_enabled,network_enabled;

@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
txtLat = (TextView) findViewById(R.id.textview1);

locationManager = (LocationManager) getSystemService(Context.LOCATION_SERVICE);
locationManager.requestLocationUpdates(LocationManager.GPS_PROVIDER, 0, 0, this);
}
@Override
public void onLocationChanged(Location location) {
txtLat = (TextView) findViewById(R.id.textview1);
txtLat.setText("Latitude:" + location.getLatitude() + ", Longitude:" + location.getLongitude());
}

@Override
public void onProviderDisabled(String provider) {
Log.d("Latitude","disable");
}

@Override
public void onProviderEnabled(String provider) {
Log.d("Latitude","enable");
}

@Override
public void onStatusChanged(String provider, int status, Bundle extras) {
Log.d("Latitude","status");
}
}

Here's a snippet that will walk the file tree for you:

indir = '/home/des/test'
for root, dirs, filenames in os.walk(indir):
    for f in filenames:
        print(f)
        log = open(indir + f, 'r')

• You must add @Bean public RestTemplate restTemplate(RestTemplateBuilder builder){ return builder.build(); }

I had a similar situation, but the client config was using a basicHttpBinding. The issue turned out to be that the service was using SOAP 1.2 and you can't specify SOAP 1.2 in a basicHttpBinding. I modified the client config to use a customBinding instead and everything worked. Here are the details of my customBinding for reference. The service I was trying to consume was over HTTPS using UserNameOverTransport.

<customBinding>
    <binding name="myBindingNameHere" sendTimeout="00:03:00">
        <security authenticationMode="UserNameOverTransport" includeTimestamp="false">
            <secureConversationBootstrap />
        </security>
        <textMessageEncoding maxReadPoolSize="64" maxWritePoolSize="16"
              messageVersion="Soap12" writeEncoding="utf-8">
            <readerQuotas maxDepth="32" maxStringContentLength="8192" maxArrayLength="16384"
                maxBytesPerRead="4096" maxNameTableCharCount="16384" />
        </textMessageEncoding>
        <httpsTransport manualAddressing="false" maxBufferPoolSize="4194304"
              maxReceivedMessageSize="4194304" allowCookies="false" authenticationScheme="Basic"
              bypassProxyOnLocal="false" hostNameComparisonMode="StrongWildcard"
              keepAliveEnabled="true" maxBufferSize="4194304" proxyAuthenticationScheme="Anonymous"
              realm="" transferMode="Buffered" unsafeConnectionNtlmAuthentication="false"
              useDefaultWebProxy="true" requireClientCertificate="false" />
    </binding>
</customBinding>

This scrip is working for all version of android and i find it after many search

LocationManager locMan;
    String[] mockProviders = {LocationManager.GPS_PROVIDER, LocationManager.NETWORK_PROVIDER};

    try {
        locMan = (LocationManager) getSystemService(Context.LOCATION_SERVICE);

        for (String p : mockProviders) {
            if (p.contentEquals(LocationManager.GPS_PROVIDER))
                locMan.addTestProvider(p, false, false, false, false, true, true, true, 1,
                        android.hardware.SensorManager.SENSOR_STATUS_ACCURACY_HIGH);
            else
                locMan.addTestProvider(p, false, false, false, false, true, true, true, 1,
                        android.hardware.SensorManager.SENSOR_STATUS_ACCURACY_LOW);

            locMan.setTestProviderEnabled(p, true);
            locMan.setTestProviderStatus(p, android.location.LocationProvider.AVAILABLE, Bundle.EMPTY,
                    java.lang.System.currentTimeMillis());
        }
    } catch (Exception ignored) {
        // here you should show dialog which is mean the mock location is not enable
    }

cat alone may not be possible, but if you don't want to use head this works:

 cat <file> | awk 'NR == 1'

This task can be accomplished without blueprints and tricky imports using Centralized URL Map

app.py

import views
from flask import Flask

app = Flask(__name__)

app.add_url_rule('/', view_func=views.index)
app.add_url_rule('/other', view_func=views.other)

if __name__ == '__main__':
    app.run(debug=True, use_reloader=True)

views.py

from flask import render_template

def index():
    return render_template('index.html')

def other():
    return render_template('other.html')

This will write a JTable to a tab separated file that can be easily imported into Excel. This works.

If you save an Excel worksheet as an XML document you could also build the XML file for EXCEL with code. I have done this with word so you do not have to use third-party packages.

This could code have the JTable taken out and then just write a tab separated to any text file and then import into Excel. I hope this helps.

Code:

import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import javax.swing.JTable;
import javax.swing.table.TableModel;

public class excel {
    String columnNames[] = { "Column 1", "Column 2", "Column 3" };

    // Create some data
    String dataValues[][] =
    {
        { "12", "234", "67" },
        { "-123", "43", "853" },
        { "93", "89.2", "109" },
        { "279", "9033", "3092" }
    };

    JTable table;

    excel() {
        table = new JTable( dataValues, columnNames );
    }


    public void toExcel(JTable table, File file){
        try{
            TableModel model = table.getModel();
            FileWriter excel = new FileWriter(file);

            for(int i = 0; i < model.getColumnCount(); i++){
                excel.write(model.getColumnName(i) + "\t");
            }

            excel.write("\n");

            for(int i=0; i< model.getRowCount(); i++) {
                for(int j=0; j < model.getColumnCount(); j++) {
                    excel.write(model.getValueAt(i,j).toString()+"\t");
                }
                excel.write("\n");
            }

            excel.close();

        }catch(IOException e){ System.out.println(e); }
    }

    public static void main(String[] o) {
        excel cv = new excel();
        cv.toExcel(cv.table,new File("C:\\Users\\itpr13266\\Desktop\\cs.tbv"));
    }
}

If you are using a LinearLayout you should call myView.bringToFront() and after you should call parentView.requestLayout() and parentView.invalidate() to force the parent to redraw with the new child order.

the problem will be solved by clearing cache

php artisan config:cache

strtotime( "+1 month", strtotime( $time ) );

this returns a timestamp that can be used with the date function

vim +21490go script.py

From the command line will open the file and take you to position 21490 in the buffer.

Triggering it from the command line like this allows you to automate a script to parse the exception message and open the file to the problem position.

Excerpt from man vim:

+{command}

-c {command}

{command} will be executed after the first file has been read. {command} is interpreted as an Ex command. If the {command} contains spaces it must be enclosed in double quotes (this depends on the shell that is used).

You can see if the file is locked by trying to read or lock it yourself first.

Please see my answer here for more information.

MySQL implicitly closed the database connection because the connection has been inactive for too long (34,247,052 milliseconds ˜ 9.5 hours). If your program then fetches a bad connection from the connection-pool that causes the MySQLNonTransientConnectionException: No operations allowed after connection closed.

MySQL suggests:

You should consider either expiring and/or testing connection validity before use in your application, increasing the server configured values for client timeouts, or using the Connector/J connection property autoReconnect=true to avoid this problem.

So that I can ask it to get me the content/text in the div tag with class='container' contained within the body tag, Or something similar.

try: 
    from BeautifulSoup import BeautifulSoup
except ImportError:
    from bs4 import BeautifulSoup
html = #the HTML code you've written above
parsed_html = BeautifulSoup(html)
print(parsed_html.body.find('div', attrs={'class':'container'}).text)

You don't need performance descriptions I guess - just read how BeautifulSoup works. Look at its official documentation.

?"[" pretty much covers the various ways of accessing elements of things.

Under usage it lists these:

x[i]
x[i, j, ... , drop = TRUE]
x[[i, exact = TRUE]]
x[[i, j, ..., exact = TRUE]]
x$name
getElement(object, name)

x[i] <- value
x[i, j, ...] <- value
x[[i]] <- value
x$i <- value

The second item is sufficient for your purpose

Under Arguments it points out that with [ the arguments i and j can be numeric, character or logical

So these work:

data[1,1]
data[1,"V1"]

As does this:

data$V1[1]

and keeping in mind a data frame is a list of vectors:

data[[1]][1]
data[["V1"]][1]

will also both work.

So that's a few things to be going on with. I suggest you type in the examples at the bottom of the help page one line at a time (yes, actually type the whole thing in one line at a time and see what they all do, you'll pick up stuff very quickly and the typing rather than copypasting is an important part of helping to commit it to memory.)

You can expend the following function in order to pull out more parameters from the DB before the insert:

--
-- insert_employee  (Function) 
--
CREATE OR REPLACE FUNCTION insert_employee(p_emp_id in number, p_emp_name in varchar2, p_emp_address in varchar2, p_emp_state in varchar2, p_emp_position in varchar2, p_emp_manager in varchar2) 
RETURN VARCHAR2 AS

   p_state_id varchar2(30) := '';
 BEGIN    
      select state_id 
      into   p_state_id
      from states where lower(emp_state) = state_name;

      INSERT INTO Employee (emp_id, emp_name, emp_address, emp_state, emp_position, emp_manager) VALUES 
                (p_emp_id, p_emp_name, p_emp_address, p_state_id, p_emp_position, p_emp_manager);

    return 'SUCCESS';

 EXCEPTION 
   WHEN others THEN
    RETURN 'FAIL';
 END;
/

They are stored in the CGI fieldstorage object.

import cgi
form = cgi.FieldStorage()

print "The user entered %s" % form.getvalue("uservalue")

I think I have to do svn info and then retrieve the number with a string manipulation from "Revision: xxxxxx" It would be just nice, if there were a command that returns just the number :)

You can specify become_method to override the default method set in ansible.cfg (if any), and which can be set to one of sudo, su, pbrun, pfexec, doas, dzdo, ksu.

- name: I am confused
  command: 'whoami'
  become: true
  become_method: su
  become_user: some_user
  register: myidentity

- name: my secret identity
  debug:
    msg: '{{ myidentity.stdout }}'

Should display

TASK [my-task : my secret identity] ************************************************************
ok: [my_ansible_server] => {
    "msg": "some_user"
}

Perhaps you might want to use "addEventListener"

document.getElementById("test").addEventListener('click',function ()
{
    foo2();
   }  ); 

Hope it's still useful for you

Try like this

String sql = "SELECT t FROM table t";
Query query = em.createQuery(sql);
query.setFirstResult(firstPosition);
query.setMaxResults(numberOfRecords);
List result = query.getResultList();

It should work

UPDATE*

You can also try like this

query.setMaxResults(1).getResultList();

import imp
imp.reload(script4)

String.format("%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X%02X", result[0], result[1], result[2], result[3], 
    result[4], result[5], result[6], result[7],
    result[8], result[9], result[10], result[11],
    result[12], result[13], result[14], result[15],
    result[16], result[17], result[18], result[19]);

Use this to lock view controller orientation, tested on IOS 9:

// Lock orientation to landscape right

-(UIInterfaceOrientationMask)supportedInterfaceOrientations {
    return UIInterfaceOrientationMaskLandscapeRight;
}

-(NSUInteger)navigationControllerSupportedInterfaceOrientations:(UINavigationController *)navigationController {
    return UIInterfaceOrientationMaskLandscapeRight;
}

You can't compare strings with == in C. For C, strings are just (zero-terminated) arrays, so you need to use string functions to compare them. See the man page for strcmp() and strncmp().

If you want to compare a character you need to compare to a character, not a string. "a" is the string a, which occupies two bytes (the a and the terminating null byte), while the character a is represented by 'a' in C.

Android documents suggests to use getApplicationContext();

but it will not work instead of that use your current activity while instantiating AlertDialog.Builder or AlertDialog or Dialog...

Ex:

AlertDialog.Builder builder = new  AlertDialog.Builder(this);

or

AlertDialog.Builder builder = new  AlertDialog.Builder((Your Activity).this);

This works for me.

@{ var dic = new Dictionary<string, string>() { { "checked", "" } }; }
@Html.RadioButtonFor(_ => _.BoolProperty, true, (@Model.BoolProperty)? dic: null) Yes
@Html.RadioButtonFor(_ => _.BoolProperty, false, ([email protected])? dic: null) No

#include <iostream>
#include <sstream>

std::string input = "abc,def,ghi";
std::istringstream ss(input);
std::string token;

while(std::getline(ss, token, ',')) {
    std::cout << token << '\n';
}

abc
def
ghi

So none of the above stuff worked for me. Except this: edit httpd.conf,

find the line

Listen 80 

and change to

listen 0.0.0.0:80 

if you are running windows 8, its got something to do with using ipv6 instead of ipv4

The answers are partially correct because @@ is actually a class variable which is per class hierarchy meaning it is shared by a class, its instances and its descendant classes and their instances.

class Person
  @@people = []

  def initialize
    @@people << self
  end

  def self.people
    @@people
  end
end

class Student < Person
end

class Graduate < Student
end

Person.new
Student.new

puts Graduate.people

This will output

#<Person:0x007fa70fa24870>
#<Student:0x007fa70fa24848>

So there is only one same @@variable for Person, Student and Graduate classes and all class and instance methods of these classes refer to the same variable.

There is another way of defining a class variable which is defined on a class object (Remember that each class is actually an instance of something which is actually the Class class but it is another story). You use @ notation instead of @@ but you can't access these variables from instance methods. You need to have class method wrappers.

class Person

  def initialize
    self.class.add_person self
  end

  def self.people
    @people
  end

  def self.add_person instance
    @people ||= []
    @people << instance
  end
end

class Student < Person
end

class Graduate < Student
end

Person.new
Person.new
Student.new
Student.new
Graduate.new
Graduate.new

puts Student.people.join(",")
puts Person.people.join(",")
puts Graduate.people.join(",")

Here, @people is single per class instead of class hierarchy because it is actually a variable stored on each class instance. This is the output:

#<Student:0x007f8e9d2267e8>,#<Student:0x007f8e9d21ff38>
#<Person:0x007f8e9d226158>,#<Person:0x007f8e9d226608>
#<Graduate:0x007f8e9d21fec0>,#<Graduate:0x007f8e9d21fdf8> 

One important difference is that, you cannot access these class variables (or class instance variables you can say) directly from instance methods because @people in an instance method would refer to an instance variable of that specific instance of the Person or Student or Graduate classes.

So while other answers correctly state that @myvariable (with single @ notation) is always an instance variable, it doesn't necessarily mean that it is not a single shared variable for all instances of that class.

Url.Action("Evil", model)

will generate a get query string but your ajax method is post and it will throw error status of 500(Internal Server Error). – Fereydoon Barikzehy Feb 14 at 9:51

Just Add "JsonRequestBehavior.AllowGet" on your Json object.

double *ptr = malloc(sizeof(double *) * TIME);
/* ... */
for(tcount = 0; tcount <= TIME; tcount++)
                         ^^

• You're overstepping the array. Either change <= to < or alloc SIZE + 1 elements
• Your malloc is wrong, you'll want sizeof(double) instead of sizeof(double *)
• As ouah comments, although not directly linked to your corruption problem, you're using *(ptr+tcount) without initializing it

• Just as a style note, you might want to use ptr[tcount] instead of *(ptr + tcount)
• You don't really need to malloc + free since you already know SIZE

I'm not familiar with either of these books, but the second is closer to current reality. The first may be discussing a specific processor.

Processors have been made with quite a variety of word sizes, not always a multiple of 8.

The 8086 and 8087 processors used 16 bit words, and it's likely this is the machine the first author was writing about.

More recent processors commonly use 32 or 64 bit words.

In the 50's and 60's there were machines with words sizes that seem quite strange to us now, such as 4, 9 and 36. Since about the 70's word size has commonly been a power of 2 and a multiple of 8.

My specific case has the following scenario. Our tests

public class VenueResourceContainerTest extends BaseTixContainerTest

all extend

BaseTixContainerTest

and JUnit was trying to run BaseTixContainerTest. Poor BaseTixContainerTest was just trying to setup the container, setup the client, order some pizza and relax... man.

As mentioned previously, you can annotate the class with

@Ignore

But that caused JUnit to report that test as skipped (as opposed to completely ignored).

Tests run: 4, Failures: 0, Errors: 0, Skipped: 1

That kind of irritated me.

So I made BaseTixContainerTest abstract, and now JUnit truly ignores it.

Tests run: 3, Failures: 0, Errors: 0, Skipped: 0

Considering there might be several img tags I would recommend re.findall:

import re

with open("sample.txt", 'r') as f_in, open('writetest.txt', 'w') as f_out:
    for line in f_in:
        for img in re.findall('<img[^>]+>', line):
            print >> f_out, "yo it's a {}".format(img)

For Ubuntu users with the same problem (e.g. Eclipse crash during debug) do a netstat -a -p | grep 8095 (or any other port number if the Tomcat server), then kill -9 that process.

As @snapshoe says

flush() sends your SQL statements to the database

commit() commits the transaction.

When session.autocommit == False:

commit() will call flush() if you set autoflush == True.

When session.autocommit == True:

You can't call commit() if you haven't started a transaction (which you probably haven't since you would probably only use this mode to avoid manually managing transactions).

In this mode, you must call flush() to save your ORM changes. The flush effectively also commits your data.

define I use for global constants.

const I use for class constants.

You cannot define into class scope, and with const you can. Needless to say, you cannot use const outside class scope.

Also, with const, it actually becomes a member of the class, and with define, it will be pushed to global scope.

2020

It's perfect date/time library called Moment.js with this library you can simply write:

moment().subtract(1,'year')

and call any format you wish:

moment().subtract(1,'year').toDate()
moment().subtract(1,'year').toISOString()

See full documentation here: https://momentjs.com/

You can easily make your own 'AlertView' and use it everywhere.

alertView("You really want this?");

Implement it once:

private void alertView( String message ) {
 AlertDialog.Builder dialog = new AlertDialog.Builder(context);
 dialog.setTitle( "Hello" )
       .setIcon(R.drawable.ic_launcher)
       .setMessage(message)
//     .setNegativeButton("Cancel", new DialogInterface.OnClickListener() {
//      public void onClick(DialogInterface dialoginterface, int i) {
//          dialoginterface.cancel();   
//          }})
      .setPositiveButton("Ok", new DialogInterface.OnClickListener() {
        public void onClick(DialogInterface dialoginterface, int i) {   
        }               
        }).show();
 }

The parseInt solution is the best way to go as it is clear what is happening.

For completeness it is worth mentioning that this can also be done with the + operator

$('.load_more').live("click",function() { //When user clicks
  var newcurrentpageTemp = +$(this).attr("id") + 1; //Get the id from the hyperlink
  alert(newcurrentpageTemp);
  dosomething();
});

I had the same problem when I wrote two upstreams in NGINX conf

upstream php_upstream {
    server unix:/var/run/php/my.site.sock;
    server 127.0.0.1:9000;
}

...

fastcgi_pass php_upstream;

but in /etc/php/7.3/fpm/pool.d/www.conf I listened the socket only

listen = /var/run/php/my.site.sock

So I need just socket, no any 127.0.0.1:9000, and I just removed IP+port upstream

upstream php_upstream {
    server unix:/var/run/php/my.site.sock;
}

This could be rewritten without an upstream

fastcgi_pass unix:/var/run/php/my.site.sock;

childView.bringToFront() didn't work for me, so I set the Z translation of the least recently added item (the one that was overlaying all other children) to a negative value like so:

lastView.setTranslationZ(-10);

see https://developer.android.com/reference/android/view/View.html#setTranslationZ(float) for more

@JasonTrue is correct, that stripping HTML tags should not be done via regular expressions.

It's quite simple to strip HTML tags using HtmlAgilityPack:

public string StripTags(string input) {
    var doc = new HtmlDocument();
    doc.LoadHtml(input ?? "");
    return doc.DocumentNode.InnerText;
}

You were probably changing the layout margin after it has been drawn. mOldTextView.invalidate() is useless. you needed to call requestLayout() on the parent to relayout the new configuration. When you moved the layout changing code before the drawing took place, everything worked fine.

Works for me too

responsive:true
maintainAspectRatio: false


<div class="row">
        <div class="col-xs-12">
            <canvas id="mycanvas" width="500" height="300"></canvas>
        </div>
    </div>

Thank You

Solution on windows : restarted docker

On windows I used --use-container option during sam build

So, in order to fix stuck process, I've restarted docker

Ubuntu:

$ sudo vi /etc/default/locale

Add below setting at the end of file.

LC_ALL = en_US.UTF-8

Here is updated working version for me which will get City/Town, It looks like some fields are modified in the json response. Referring previous answers for this questions. ( Thanks to Michal & one more reference : Link

var geocoder;

if (navigator.geolocation) {
  navigator.geolocation.getCurrentPosition(successFunction, errorFunction);
}
// Get the latitude and the longitude;
function successFunction(position) {
  var lat = position.coords.latitude;
  var lng = position.coords.longitude;
  codeLatLng(lat, lng);
}

function errorFunction() {
  alert("Geocoder failed");
}

function initialize() {
  geocoder = new google.maps.Geocoder();

}

function codeLatLng(lat, lng) {
  var latlng = new google.maps.LatLng(lat, lng);
  geocoder.geocode({latLng: latlng}, function(results, status) {
    if (status == google.maps.GeocoderStatus.OK) {
      if (results[1]) {
        var arrAddress = results;
        console.log(results);
        $.each(arrAddress, function(i, address_component) {
          if (address_component.types[0] == "locality") {
            console.log("City: " + address_component.address_components[0].long_name);
            itemLocality = address_component.address_components[0].long_name;
          }
        });
      } else {
        alert("No results found");
      }
    } else {
      alert("Geocoder failed due to: " + status);
    }
  });
}

This is the solution (from this post)

video::-internal-media-controls-download-button {
    display:none;
}

video::-webkit-media-controls-enclosure {
    overflow:hidden;
}

video::-webkit-media-controls-panel {
    width: calc(100% + 30px); /* Adjust as needed */
}

Update 2 : New Solution by @Remo

<video width="512" height="380" controls controlsList="nodownload">
    <source data-src="mov_bbb.ogg" type="video/mp4">
</video>

To wait for visibility

const EC = protractor.ExpectedConditions;
browser.wait(EC.visibilityOf(element(by.css('.icon-spinner icon-spin ng-hide')))).then(function() {
  //do stuff
})

Xpath trick to only find visible elements

element(by.xpath('//i[not(contains(@style,"display:none")) and @class="icon-spinner icon-spin ng-hide"]))

You can use input text with "list" attribute, which refers to the datalist of values.

<input type="text" name="city" list="cityname">_x000D_
    <datalist id="cityname">_x000D_
      <option value="Boston">_x000D_
      <option value="Cambridge">_x000D_
    </datalist>

This creates a free text input field that also has a drop-down to select predefined choices. Attribution for example and more information: https://www.w3.org/wiki/HTML/Elements/datalist

This is a very late answer,but this might help.I went to this link and searched for ojdbc8(I was trying to add jdbc oracle driver) When clicked on the result , a note was displayed like this:

I clicked the link in the note and the correct dependency was mentioned like below

Use double braces {{ or }} so your code becomes:

sb.AppendLine(String.Format("public {0} {1} {{ get; private set; }}", 
prop.Type, prop.Name));

// For prop.Type of "Foo" and prop.Name of "Bar", the result would be:
// public Foo Bar { get; private set; }

I had the same problem and found a very elegant solution for a Pager Class from

http://blogs.taiga.nl/martijn/2008/08/27/paging-with-aspnet-mvc/

In your controller the call looks like:

return View(partnerList.ToPagedList(currentPageIndex, pageSize));

and in your view:

<div class="pager">
    Seite: <%= Html.Pager(ViewData.Model.PageSize, 
                          ViewData.Model.PageNumber,
                          ViewData.Model.TotalItemCount)%>
</div>

encoding this line fixed it for me.

m.update(line.encode('utf-8'))

And this:

FirebaseInstanceId.getInstance().getInstanceId().getResult().getToken()

suppose to be solution of deprecated:

FirebaseInstanceId.getInstance().getToken()

EDIT

FirebaseInstanceId.getInstance().getInstanceId().getResult().getToken() can produce exception if the task is not yet completed, so the method witch Nilesh Rathod described (with .addOnSuccessListener) is correct way to do it.

Kotlin:

FirebaseInstanceId.getInstance().instanceId.addOnSuccessListener(this) { instanceIdResult ->
        val newToken = instanceIdResult.token
        Log.e("newToken", newToken)
    }

It's because size_t can be anything other than an int (maybe a struct). The idea is that it decouples it's job from the underlying type.

In my case, in order to delete a heavy schema from mysql server, just went to C:\ProgramData\MySQL\MySQL Server 5.7\Data and deleted relevant folder. But it was not being deleted because mysqld.exe was preventing it. so I stopped mysqld.exe, deleted the folder and then all the schemas went disappeared from the list in mysql workbench. No matter how much I tried to restart mysql service, it didnt unless I restored that folder from junk. Hope it helps someone who tried the same shortcut as I did.

Just two more things I found helpful to know, even if they are not part of the question, really.

You can use the relayEvents method to tell a component to listen for certain events of another component and then fire them again as if they originate from the first component. The API docs give the example of a grid relaying the store load event. It is quite handy when writing custom components that encapsulate several sub-components.

The other way around, i.e. passing on events received by an encapsulating component mycmp to one of its sub-components subcmp, can be done like this

mycmp.on('show' function (mycmp, eOpts)
{
   mycmp.subcmp.fireEvent('show', mycmp.subcmp, eOpts);
});

If you need to sync files between two remote nodes via ansible you can use this:

- name: synchronize between nodes
  environment:
    RSYNC_PASSWORD: "{{ input_user_password_if_needed }}"
  synchronize:
    src: rsync://user@remote_server:/module/
    dest: /destination/directory/
    // if needed
    rsync_opts:
       - "--include=what_needed"
       - "--exclude=**/**"
    mode: pull
    delegate_to: "{{ inventory_hostname }}"

when on remote_server you need to startup rsync with daemon mode. Simple example:

pid file = /var/run/rsyncd.pid
lock file = /var/run/rsync.lock
log file = /var/log/rsync.log
port = port

[module]
path = /path/to/needed/directory/
uid = nobody
gid = nobody
read only = yes
list = yes
auth users = user
secrets file = /path/to/secret/file

pandas >= 1.0

Here's a chart that summarises some of the most important conversions in pandas.

Conversions to string are trivial .astype(str) and are not shown in the figure.

"Hard" versus "Soft" conversions

Note that "conversions" in this context could either refer to converting text data into their actual data type (hard conversion), or inferring more appropriate data types for data in object columns (soft conversion). To illustrate the difference, take a look at

df = pd.DataFrame({'a': ['1', '2', '3'], 'b': [4, 5, 6]}, dtype=object)
df.dtypes                                                                  

a    object
b    object
dtype: object

# Actually converts string to numeric - hard conversion
df.apply(pd.to_numeric).dtypes                                             

a    int64
b    int64
dtype: object

# Infers better data types for object data - soft conversion
df.infer_objects().dtypes                                                  

a    object  # no change
b     int64
dtype: object

# Same as infer_objects, but converts to equivalent ExtensionType
df.convert_dtypes().dtypes                                                     

Use the following code:

JSONObject student1 = new JSONObject();
try {
    student1.put("id", "3");
    student1.put("name", "NAME OF STUDENT");
    student1.put("year", "3rd");
    student1.put("curriculum", "Arts");
    student1.put("birthday", "5/5/1993");

} catch (JSONException e) {
    // TODO Auto-generated catch block
    e.printStackTrace();
}

JSONObject student2 = new JSONObject();
try {
    student2.put("id", "2");
    student2.put("name", "NAME OF STUDENT2");
    student2.put("year", "4rd");
    student2.put("curriculum", "scicence");
    student2.put("birthday", "5/5/1993");

} catch (JSONException e) {
    // TODO Auto-generated catch block
    e.printStackTrace();
}


JSONArray jsonArray = new JSONArray();

jsonArray.put(student1);
jsonArray.put(student2);

JSONObject studentsObj = new JSONObject();
    studentsObj.put("Students", jsonArray);



String jsonStr = studentsObj.toString();

    System.out.println("jsonString: "+jsonStr);

Most problems can be solved using (i) just locks, (ii) just semaphores, ..., or (iii) a combination of both! As you may have discovered, they're very similar: both prevent race conditions, both have acquire()/release() operations, both cause zero or more threads to become blocked/suspected... Really, the crucial difference lies solely on how they lock and unlock.

• A lock (or mutex) has two states (0 or 1). It can be either unlocked or locked. They're often used to ensure only one thread enters a critical section at a time.
• A semaphore has many states (0, 1, 2, ...). It can be locked (state 0) or unlocked (states 1, 2, 3, ...). One or more semaphores are often used together to ensure that only one thread enters a critical section precisely when the number of units of some resource has/hasn't reached a particular value (either via counting down to that value or counting up to that value).

For both locks/semaphores, trying to call acquire() while the primitive is in state 0 causes the invoking thread to be suspended. For locks - attempts to acquire the lock is in state 1 are successful. For semaphores - attempts to acquire the lock in states {1, 2, 3, ...} are successful.

For locks in state state 0, if same thread that had previously called acquire(), now calls release, then the release is successful. If a different thread tried this -- it is down to the implementation/library as to what happens (usually the attempt ignored or an error is thrown). For semaphores in state 0, any thread can call release and it will be successful (regardless of which thread previous used acquire to put the semaphore in state 0).

From the preceding discussion, we can see that locks have a notion of an owner (the sole thread that can call release is the owner), whereas semaphores do not have an owner (any thread can call release on a semaphore).

What causes a lot of confusion is that, in practice they are many variations of this high-level definition.

Important variations to consider:

• What should the acquire()/release() be called? -- [Varies massively]
• Does your lock/semaphore use a "queue" or a "set" to remember the threads waiting?
• Can your lock/semaphore be shared with threads of other processes?
• Is your lock "reentrant"? -- [Usually yes].
• Is your lock "blocking/non-blocking"? -- [Normally non-blocking are used as blocking locks (aka spin-locks) cause busy waiting].
• How do you ensure the operations are "atomic"?

These depends on your book / lecturer / language / library / environment.
Here's a quick tour noting how some languages answer these details.

C, C++ (pthreads)

• A mutex is implemented via pthread_mutex_t. By default, they can't be shared with any other processes (PTHREAD_PROCESS_PRIVATE), however mutex's have an attribute called pshared. When set, so the mutex is shared between processes (PTHREAD_PROCESS_SHARED).
• A lock is the same thing as a mutex.
• A semaphore is implemented via sem_t. Similar to mutexes, semaphores can be shared between threasds of many processes or kept private to the threads of one single process. This depends on the pshared argument provided to sem_init.

python (threading.py)

• A lock (threading.RLock) is mostly the same as C/C++ pthread_mutex_ts. Both are both reentrant. This means they may only be unlocked by the same thread that locked it. It is the case that sem_t semaphores, threading.Semaphore semaphores and theading.Lock locks are not reentrant -- for it is the case any thread can perform unlock the lock / down the semaphore.
• A mutex is the same as a lock (the term is not used often in python).
• A semaphore (threading.Semaphore) is mostly the same as sem_t. Although with sem_t, a queue of thread ids is used to remember the order in which threads became blocked when attempting to lock it while it is locked. When a thread unlocks a semaphore, the first thread in the queue (if there is one) is chosen to be the new owner. The thread identifier is taken off the queue and the semaphore becomes locked again. However, with threading.Semaphore, a set is used instead of a queue, so the order in which threads became blocked is not stored -- any thread in the set may be chosen to be the next owner.

Java (java.util.concurrent)

• A lock (java.util.concurrent.ReentrantLock) is mostly the same as C/C++ pthread_mutex_t's, and Python's threading.RLock in that it also implements a reentrant lock. Sharing locks between processes is harder in Java because of the JVM acting as an intermediary. If a thread tries to unlock a lock it doesn't own, an IllegalMonitorStateException is thrown.
• A mutex is the same as a lock (the term is not used often in Java).
• A semaphore (java.util.concurrent.Semaphore) is mostly the same as sem_t and threading.Semaphore. The constructor for Java semaphores accept a fairness boolean parameter that control whether to use a set (false) or a queue (true) for storing the waiting threads.

In theory, semaphores are often discussed, but in practice, semaphores aren't used so much. A semaphore only hold the state of one integer, so often it's rather inflexible and many are needed at once -- causing difficulty in understanding code. Also, the fact that any thread can release a semaphore is sometimes undesired. More object-oriented / higher-level synchronization primitives / abstractions such as "condition variables" and "monitors" are used instead.

This should work:

/^((?!PART).)*$/

If you only wanted to exclude it from the beginning of the line (I know you don't, but just FYI), you could use this:

/^(?!PART)/

Edit (by request): Why this pattern works

The (?!...) syntax is a negative lookahead, which I've always found tough to explain. Basically, it means "whatever follows this point must not match the regular expression /PART/." The site I've linked explains this far better than I can, but I'll try to break this down:

^         #Start matching from the beginning of the string.    
(?!PART)  #This position must not be followed by the string "PART".
.         #Matches any character except line breaks (it will include those in single-line mode).
$         #Match all the way until the end of the string.

The ((?!xxx).)* idiom is probably hardest to understand. As we saw, (?!PART) looks at the string ahead and says that whatever comes next can't match the subpattern /PART/. So what we're doing with ((?!xxx).)* is going through the string letter by letter and applying the rule to all of them. Each character can be anything, but if you take that character and the next few characters after it, you'd better not get the word PART.

The ^ and $ anchors are there to demand that the rule be applied to the entire string, from beginning to end. Without those anchors, any piece of the string that didn't begin with PART would be a match. Even PART itself would have matches in it, because (for example) the letter A isn't followed by the exact string PART.

Since we do have ^ and $, if PART were anywhere in the string, one of the characters would match (?=PART). and the overall match would fail. Hope that's clear enough to be helpful.

For people wanting to use the built-in .NET SmtpClient rather than the SendGrid client library (not sure if that was the OP's intent), I couldn't get it to work unless I used apikey as my username and the api key itself as the password as outlined here.

<mailSettings>
    <smtp>
        <network host="smtp.sendgrid.net" port="587" userName="apikey" password="<your key goes here>" />
    </smtp>
</mailSettings>

This syntax

--exclude-dir={dir1,dir2}

is expanded by the shell (e.g. Bash), not by grep, into this:

--exclude-dir=dir1 --exclude-dir=dir2

Quoting will prevent the shell from expanding it, so this won't work:

--exclude-dir='{dir1,dir2}'    <-- this won't work

The patterns used with --exclude-dir are the same kind of patterns described in the man page for the --exclude option:

--exclude=GLOB
    Skip files whose base name matches GLOB (using wildcard matching).
    A file-name glob can use *, ?, and [...]  as wildcards, and \ to
    quote a wildcard or backslash character literally.

The shell will generally try to expand such a pattern itself, so to avoid this, you should quote it:

--exclude-dir='dir?'

You can use the curly braces and quoted exclude patterns together like this:

--exclude-dir={'dir?','dir??'}

A pattern can span multiple path segments:

--exclude-dir='some*/?lse'

This would exclude a directory like topdir/something/else.

Here is an easy way.

@Test
void exceptionTest() {

   try{
        model.someMethod("invalidInput");
        fail("Exception Expected!");
   }
   catch(SpecificException e){

        assertTrue(true);
   }
   catch(Exception e){
        fail("wrong exception thrown");
   }

}

It only succeeds when the Exception you expect is thrown.

I would recommend having a look at this answer of mine, and see if it is relevant to what you are doing. If I understand your real problem, it's that your just not using your async action correctly and updating the redux "store", which will automatically update your component with it's new props.

This section of your code:

componentDidMount() {
      if (this.props.isManager) {
        this.props.dispatch(actions.fetchAllSites())
      } else {
        const currentUserId = this.props.user.get('id')
        this.props.dispatch(actions.fetchUsersSites(currentUserId))
      }  
    }

Should not be triggering in a component, it should be handled after executing your first request.

Have a look at this example from redux-thunk:

function makeASandwichWithSecretSauce(forPerson) {

  // Invert control!
  // Return a function that accepts `dispatch` so we can dispatch later.
  // Thunk middleware knows how to turn thunk async actions into actions.

  return function (dispatch) {
    return fetchSecretSauce().then(
      sauce => dispatch(makeASandwich(forPerson, sauce)),
      error => dispatch(apologize('The Sandwich Shop', forPerson, error))
    );
  };
}

You don't necessarily have to use redux-thunk, but it will help you reason about scenarios like this and write code to match.

All of the rules concerning the encoding of URIs (which contains URNs and URLs) are specified in the RFC1738 and the RFC3986, here's a TL;DR of these long and boring documents:

Percent-encoding, also known as URL encoding, is a mechanism for encoding information in a URI under certain circumstances. The characters allowed in a URI are either reserved or unreserved. Reserved characters are those characters that sometimes have special meaning, but they are not the only characters that needs encoding.

There are 66 unreserved characters that doesn't need any encoding: abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-_.~

There are 18 reserved characters which needs to be encoded: !*'();:@&=+$,/?#[], and all the other characters must be encoded.

To percent-encode a character, simply concatenate "%" and its ASCII value in hexadecimal. The php functions "urlencode" and "rawurlencode" do this job for you.

I know this is kind of old but if you are reading the contents of a SqlDataReader into a class, then this will be very handy. the column names of reader and class should be same

public static List<T> Fill<T>(this SqlDataReader reader) where T : new()
        {
            List<T> res = new List<T>();
            while (reader.Read())
            {
                T t = new T();
                for (int inc = 0; inc < reader.FieldCount; inc++)
                {
                    Type type = t.GetType();
                    string name = reader.GetName(inc);
                    PropertyInfo prop = type.GetProperty(name);
                    if (prop != null)
                    {
                        if (name == prop.Name)
                        {
                            var value = reader.GetValue(inc);
                            if (value != DBNull.Value)
                            { 
                                prop.SetValue(t, Convert.ChangeType(value, prop.PropertyType), null);
                            }
                            //prop.SetValue(t, value, null);

                        }
                    }
                }
                res.Add(t);
            }
            reader.Close();

            return res;
        }

If you are using kotlin then the context will be already defined in the fragment. So just use that context. Try the following code to show a toast message.

Toast.makeText(context , "your_text", Toast.LENGTH_SHORT).show()

You can use a for loop. but don't forget the last char must be a null character !

char * msg = new char[65546];
for(int i=0;i<65545;i++)
{
    msg[i]='0';
}
msg[65545]='\0';

It appears that the Internet Explorer driver does not interact with everything in the same way the other drivers do and checkboxes is one of those cases.

The trick with checkboxes is to send the Space key instead of using a click (only needed on Internet Explorer), like so in C#:

if (driver.Capabilities.BrowserName.Equals(“internet explorer"))
    driver.findElement(By.id("idOfTheElement").SendKeys(Keys.Space);
else
    driver.findElement(By.id("idOfTheElement").Click();

Just in case you arrived here because you copied a branch name from Github, note that a remote branch is not automatically also a local branch, so a merge will not work and give the "not something we can merge" error.

In that case, you have two options:

git checkout [branchYouWantToMergeInto]
git merge origin/[branchYouWantToMerge]

or

# this creates a local branch
git checkout [branchYouWantToMerge]

git checkout [branchYouWantToMergeInto]
git merge [branchYouWantToMerge]

This really works - i had verified lot of sites and finally got the answer.

This may occurs when the master.mdf or the mastlog.ldf gets corrupt . In order to solve the issue goto the following path.

C:\Program Files\Microsoft SQL Server\MSSQL10_50.MSSQLSERVER\MSSQL , there you will find a folder ” Template Data ” , copy the master.mdf and mastlog.ldf and replace it in

C:\Program Files\Microsoft SQL Server\MSSQL10_50.MSSQLSERVER\MSSQL\DATA folder .

That's it. Now start the MS SQL service and you are done.

No there is no byte data type in C++. However you could always include the bitset header from the standard library and create a typedef for byte:

typedef bitset<8> BYTE;

NB: Given that WinDef.h defines BYTE for windows code, you may want to use something other than BYTE if your intending to target Windows.

Edit: In response to the suggestion that the answer is wrong. The answer is not wrong. The question was "Is there a 'byte' data type in C++?". The answer was and is: "No there is no byte data type in C++" as answered.

With regards to the suggested possible alternative for which it was asked why is the suggested alternative better?

According to my copy of the C++ standard, at the time:

"Objects declared as characters (char) shall be large enough to store any member of the implementations basic character set": 3.9.1.1

I read that to suggest that if a compiler implementation requires 16 bits to store a member of the basic character set then the size of a char would be 16 bits. That today's compilers tend to use 8 bits for a char is one thing, but as far as I can tell there is certainly no guarantee that it will be 8 bits.

On the other hand, "the class template bitset<N> describes an object that can store a sequence consisting of a fixed number of bits, N." : 20.5.1. In otherwords by specifying 8 as the template parameter I end up with an object that can store a sequence consisting of 8 bits.

Whether or not the alternative is better to char, in the context of the program being written, therefore depends, as far as I understand, although I may be wrong, upon your compiler and your requirements at the time. It was therefore upto the individual writing the code, as far as I'm concerned, to do determine whether the suggested alternative was appropriate for their requirements/wants/needs.

Edit / Update:

If you are using Typescript 3.7 or newer you can now also do:

    const data = change?.after?.data();

    if(!data) {
      console.error('No data here!');
       return null
    }

    const maxLen = 100;
    const msgLen = data.messages.length;
    const charLen = JSON.stringify(data).length;

    const batch = db.batch();

    if (charLen >= 10000 || msgLen >= maxLen) {

      // Always delete at least 1 message
      const deleteCount = msgLen - maxLen <= 0 ? 1 : msgLen - maxLen
      data.messages.splice(0, deleteCount);

      const ref = db.collection("chats").doc(change.after.id);

      batch.set(ref, data, { merge: true });

      return batch.commit();
    } else {
      return null;
    }

Original Response

Typescript is saying that change or data is possibly undefined (depending on what onUpdate returns).

So you should wrap it in a null/undefined check:

if(change && change.after && change.after.data){
    const data = change.after.data();

    const maxLen = 100;
    const msgLen = data.messages.length;
    const charLen = JSON.stringify(data).length;

    const batch = db.batch();

    if (charLen >= 10000 || msgLen >= maxLen) {

      // Always delete at least 1 message
      const deleteCount = msgLen - maxLen <= 0 ? 1 : msgLen - maxLen
      data.messages.splice(0, deleteCount);

      const ref = db.collection("chats").doc(change.after.id);

      batch.set(ref, data, { merge: true });

      return batch.commit();
    } else {
      return null;
    }
}

If you are 100% sure that your object is always defined then you can put this:

const data = change.after!.data();

Drop the 's' off of the package name.

You want sudo apt-get install build-essential

You may also need to run sudo apt-get update to make sure that your package index is up to date.

For anyone wondering why this package may be needed as part of another install, it contains the essential tools for building most other packages from source (C/C++ compiler, libc, and make).

The only way to get the iOS dictation is to sign up yourself through Nuance: http://dragonmobile.nuancemobiledeveloper.com/ - it's expensive, because it's the best. Presumably, Apple's contract prevents them from exposing an API.

The built in iOS accessibility features allow immobilized users to access dictation (and other keyboard buttons) through tools like VoiceOver and Assistive Touch. It may not be worth reinventing this if your users might be familiar with these tools.

use

$imageString = file_get_contents("http://example.com/image.jpg");
$save = file_put_contents('Image/saveto/image.jpg',$imageString);

You can use method getDate():

$('#calendar').datepicker({
    dateFormat: 'yy-m-d',
    inline: true,
    onSelect: function(dateText, inst) { 
        var date = $(this).datepicker('getDate'),
            day  = date.getDate(),  
            month = date.getMonth() + 1,              
            year =  date.getFullYear();
        alert(day + '-' + month + '-' + year);
    }
});

FIDDLE

Whilst the accepted answer works and is good for Linq to Objects it bugged me that the SQL query isn't just a straight Left Outer Join.

The following code relies on the LinkKit Project that allows you to pass expressions and invoke them to your query.

static IQueryable<TResult> LeftOuterJoin<TSource,TInner, TKey, TResult>(
     this IQueryable<TSource> source, 
     IQueryable<TInner> inner, 
     Expression<Func<TSource,TKey>> sourceKey, 
     Expression<Func<TInner,TKey>> innerKey, 
     Expression<Func<TSource, TInner, TResult>> result
    ) {
    return from a in source.AsExpandable()
            join b in inner on sourceKey.Invoke(a) equals innerKey.Invoke(b) into c
            from d in c.DefaultIfEmpty()
            select result.Invoke(a,d);
}

It can be used as follows

Table1.LeftOuterJoin(Table2, x => x.Key1, x => x.Key2, (x,y) => new { x,y});

This error could be thrown in the following situation as well.

You want to checkout branch called feature from remote repository but the error is thrown because you already have branch called feature/<feature_name> in your local repository.

Simply checkout the feature branch under a different name:

git checkout -b <new_branch_name> <remote>/feature

get_or_create() returns a tuple:

customer.source, created  = Source.objects.get_or_create(name="Website")

• created ? has a boolean value, is created or not.

• customer.source ? has an object of get_or_create() method.

exit() should always be called with an integer value and non-zero values are used as error codes.

See also: Use of exit() function

If you want to print decimal variables:

wchar_t text_buffer[20] = { 0 }; //temporary buffer
swprintf(text_buffer, _countof(text_buffer), L"%d", your.variable); // convert
OutputDebugString(text_buffer); // print

The following is correct for a WHERE clause; to make a function wrap it in CASE WHEN.

 ISNUMERIC(table.field) > 0 AND PATINDEX('%[^0123456789]%', table.field) = 0

This is a maven specific problem I think. Maven does not copy the files form /src/main/resources to the target-test folder. You will have to do this yourself by configuring the resources plugin, if you absolutely want to go this way.

An easier way is to instead put a test specific context definition in the /src/test/resources directory and load via:

@ContextConfiguration(locations = { "classpath:mycontext.xml" })

VB6: Listview1.selecteditem

VB10: Listview1.FocusedItem.Text

I was happy at my textpad and ecplise world until i had to start working with servers running under linux. Remote scripting and set up of config files was needed!

It was hard at the begining but now i can easily set up and tune up my servers.

Take a look at the Cursor.Position Property. It should get you started.

private void MoveCursor()
{
   // Set the Current cursor, move the cursor's Position,
   // and set its clipping rectangle to the form. 

   this.Cursor = new Cursor(Cursor.Current.Handle);
   Cursor.Position = new Point(Cursor.Position.X - 50, Cursor.Position.Y - 50);
   Cursor.Clip = new Rectangle(this.Location, this.Size);
}

An example of Dave Syer's answer:

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.web.servlet.config.annotation.ViewControllerRegistry;
import org.springframework.web.servlet.config.annotation.WebMvcConfigurerAdapter;

@Configuration
public class MyWebMvcConfig {

    @Bean
    public WebMvcConfigurerAdapter forwardToIndex() {
        return new WebMvcConfigurerAdapter() {
            @Override
            public void addViewControllers(ViewControllerRegistry registry) {
                // forward requests to /admin and /user to their index.html
                registry.addViewController("/admin").setViewName(
                        "forward:/admin/index.html");
                registry.addViewController("/user").setViewName(
                        "forward:/user/index.html");
            }
        };
    }

}

This kind of depends on what you want to do with the results. If you're just after the numbers, a set-based option would be a numbers table - which comes in handy for all sorts of things.

For MSSQL 2005+, you can use a recursive CTE to generate a numbers table inline:

;WITH Numbers (N) AS (
    SELECT 1 UNION ALL
    SELECT 1 + N FROM Numbers WHERE N < 500 
)
SELECT N FROM Numbers
OPTION (MAXRECURSION 500)

Documentation here, and I'll use the Frankfurt region as an example.

There are 2 different URL styles:

• Virtual host style: https://BUCKET.s3.amazonaws.com/FILE
• Path style: https://s3.eu-central-1.amazonaws.com/BUCKET/FILE

But this url does not work:

• https://s3.amazonaws.com/BUCKET/FILE

The message is explicit: The bucket you are attempting to access must be addressed using the specified endpoint. Please send all future requests to this endpoint.

I may be talking about another problem because I'm not getting NoSuchKey error but I suspect the error message has been made clearer over time.

Use isinstance:

if isinstance(e, list):

If you want to check that an object is a list or a tuple, pass several classes to isinstance:

if isinstance(e, (list, tuple)):

This is how I installed it on my machine (ubuntu):

php 7:

sudo apt-get install php7.0-zip

php 5:

sudo apt-get install php5-zip

Edit:
Make sure to restart your server afterwards.

sudo /etc/init.d/apache2 restart or sudo service nginx restart

PS: If you are using centOS, please check above cweiske's answer
But if you are using a Debian derivated OS, this solution should help you installing php zip extension.

DML have to be committed or rollbacked. DDL cannot.

http://www.orafaq.com/faq/what_are_the_difference_between_ddl_dml_and_dcl_commands

You can switch auto-commit on and that's again only for DML. DDL are never part of transactions and therefore there is nothing like an explicit commit/rollback.

truncate is DDL and therefore commited implicitly.

Edit
I've to say sorry. Like @DCookie and @APC stated in the comments there exist sth like implicit commits for DDL. See here for a question about that on Ask Tom. This is in contrast to what I've learned and I am still a bit curious about.

You can use ::after to create a 0px-height block after the <h4>, which effectively moves anything after the <h4> to the next line:

h4 {_x000D_
  display: inline;_x000D_
}_x000D_
h4::after {_x000D_
  content: "";_x000D_
  display: block;_x000D_
}

<ul>_x000D_
  <li>_x000D_
    Text, text, text, text, text. <h4>Sub header</h4>_x000D_
    Text, text, text, text, text._x000D_
  </li>_x000D_
</ul>

If you program is using threads (concurrent programming), it's not necessarily going to be executed as such (parallel execution), since it depends on whether the machine can handle several threads.

Here's a visual example. Threads on a non-threaded machine:

        --  --  --
     /              \
>---- --  --  --  -- ---->>

Threads on a threaded machine:

     ------
    /      \
>-------------->>

The dashes represent executed code. As you can see, they both split up and execute separately, but the threaded machine can execute several separate pieces at once.

The only one reason why you get some error like that, it's because your node version is not compatible with your node-sass version.

So, make sure to checkout the documentation at here: https://www.npmjs.com/package/node-sass

Or this image below will be help you, what the node version can use the node-sass version.

For an example, if you're using node version 12 on your windows ("maybe"), then you should have to install the node-sass version 4.12.

npm install [email protected]

Yeah, like that. So now you only need to install the node-sass version recommended by the node-sass team with the nodes installed on your computer.

this worked great:

UPDATE
    table_Name
SET 
  column_A = CASE WHEN @flag = '1' THEN column_A + @new_value ELSE column_A END,
  column_B = CASE WHEN @flag = '0' THEN column_B + @new_value ELSE column_B END
WHERE
    ID = @ID

var http = require('http');
var url = process.argv[2];

http.get(url, function(response) {
  var finalData = "";

  response.on("data", function (data) {
    finalData += data.toString();
  });

  response.on("end", function() {
    console.log(finalData.length);
    console.log(finalData.toString());
  });

});

Finally found a good solution to this on the dev mailing list:

In the view add:

from django.forms.models import model_to_dict

def show(request, object_id):
    object = FooForm(data=model_to_dict(Foo.objects.get(pk=object_id)))
    return render_to_response('foo/foo_detail.html', {'object': object})

in the template add:

{% for field in object %}
    <li><b>{{ field.label }}:</b> {{ field.data }}</li>
{% endfor %}

Use logging module (http://docs.python.org/library/logging.html):

import logging

logger = logging.getLogger('scope.name')

file_log_handler = logging.FileHandler('logfile.log')
logger.addHandler(file_log_handler)

stderr_log_handler = logging.StreamHandler()
logger.addHandler(stderr_log_handler)

# nice output format
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_log_handler.setFormatter(formatter)
stderr_log_handler.setFormatter(formatter)

logger.info('Info message')
logger.error('Error message')

import { Route, Redirect } from "react-router-dom";

class App extends Component {
  render() {
    return (
      <div>
        <Route path='/'>
          <Redirect to="/something" />
        </Route>
//rest of code here

this will make it so that when you load up the server on local host it will re direct you to /something

  int month = Convert.ToInt32(ddlMonth.SelectedValue);/*Store month Value From page*/
  int year = Convert.ToInt32(txtYear.Value);/*Store Year Value From page*/
  int days = System.DateTime.DaysInMonth(year, month); /*this will store no. of days for month, year that we store*/

Remove these two lines:

xmlHttp.setRequestHeader("Content-length", params.length);
xmlHttp.setRequestHeader("Connection", "close");

XMLHttpRequest isn't allowed to set these headers, they are being set automatically by the browser. The reason is that by manipulating these headers you might be able to trick the server into accepting a second request through the same connection, one that wouldn't go through the usual security checks - that would be a security vulnerability in the browser.

I wasn't satisfied with the marked and upvoted answers, so here is a simple and general solution for transforming JSON-style nested datastructures (made of dicts and lists) into hierachies of plain objects:

# tested in: Python 3.8
from collections import abc
from typings import Any, Iterable, Mapping, Union

class DataObject:
    def __repr__(self):
        return str({k: v for k, v in vars(self).items()})

def data_to_object(data: Union[Mapping[str, Any], Iterable]) -> object:
    """
    Example
    -------
    >>> data = {
    ...     "name": "Bob Howard",
    ...     "positions": [{"department": "ER", "manager_id": 13}],
    ... }
    ... data_to_object(data).positions[0].manager_id
    13
    """
    if isinstance(data, abc.Mapping):
        r = DataObject()
        for k, v in data.items():
            if type(v) is dict or type(v) is list:
                setattr(r, k, data_to_object(v))
            else:
                setattr(r, k, v)
        return r
    elif isinstance(data, abc.Iterable):
        return [data_to_object(e) for e in data]
    else:
        return data

I found possible answer. You have core-js version 3.0, and this version doesn't have separate folders for ES6 and ES7; that's why the application cannot find correct paths.

To resolve this error, you can downgrade the core-js version to 2.5.7. This version produces correct catalogs structure, with separate ES6 and ES7 folders.

To downgrade the version, simply run:

npm i -S [email protected]

In my case, with Angular, this works ok.

Searching in NPM registry https://npmjs.org/search?q=server, I have found static-server https://github.com/maelstrom/static-server

Ever needed to send a colleague a file, but can't be bothered emailing the 100MB beast? Wanted to run a simple example JavaScript application, but had problems with running it through the file:/// protocol? Wanted to share your media directory at a LAN without setting up Samba, or FTP, or anything else requiring you to edit configuration files? Then this file server will make your life that little bit easier.

To install the simple static stuff server, use npm:

npm install -g static-server

Then to serve a file or a directory, simply run

$ serve path/to/stuff
Serving path/to/stuff on port 8001

That could even list folder content.

Unfortunately, it couldn't serve files :)

If you're running Angular 2 through ASP.NET Core 1 in Visual Studio 2015, you might find this solution from Jürgen Gutsch helpful. He describes it in a blog post. It was the best solution for me. Place the C# code provided below in your Startup.cs public void Configure() just before app.UseStaticFiles();

app.Use( async ( context, next ) => {
    await next();

    if( context.Response.StatusCode == 404 && !Path.HasExtension( context.Request.Path.Value ) ) {
        context.Request.Path = "/index.html";
        await next();
    }
});

try this code

<script src="//ajax.googleapis.com/ajax/libs/jquery/1.11.0/jquery.min.js"></script>
<Script>
$(document).ready(function(){

$("#postcontent").click(function(e) {

        $.ajax({type:"POST",url:"add_new_post.php",data:$("#postcontent").serialize(),beforeSend:function(){
            $(".post_submitting").show().html("<center><img src='images/loading.gif'/></center>");
        },success:function(response){   
                    //alert(response);
                    $("#return_update_msg").html(response); 
                    $(".post_submitting").fadeOut(1000);                
            }
        });
   });
});
   </script>

<form name="postcontent" id="postcontent">
              <input name="postsubmit" type="button" id="postsubmit" value="POST"/>
              <textarea id="postdata" name="postdata" placeholder="What's Up ?"></textarea>
 </form>

What I did was save a reference to the Menu at onCreateOptionsMenu. This is similar to nir's answer except instead of saving each individual item, I saved the entire menu.

Declare a Menu Menu toolbarMenu;.

Then in onCreateOptionsMenusave the menu to your variable

@Override
public boolean onCreateOptionsMenu(Menu menu)
{
    getMenuInflater().inflate(R.menu.main_menu, menu);
    toolbarMenu = menu;
    return true;
}

Now you can access your menu and all of its items anytime you want. toolbarMenu.getItem(0).setEnabled(false);

Default values should start from the year 1000.

For example,

ALTER TABLE mytable last_active DATETIME DEFAULT '1000-01-01 00:00:00'

Hope this helps someone.

The general pattern for search and replace is:

:s/search/replace/

Replaces the first occurrence of 'search' with 'replace' for current line

:s/search/replace/g

Replaces all occurrences of 'search' with 'replace' for current line, 'g' is short for 'global'

This command will replace each occurrence of 'search' with 'replace' for the current line only. The % is used to search over the whole file. To confirm each replacement interactively append a 'c' for confirm:

:%s/search/replace/c

Interactive confirm replacing 'search' with 'replace' for the entire file

Instead of the % character you can use a line number range (note that the '^' character is a special search character for the start of line):

:14,20s/^/#/

Inserts a '#' character at the start of lines 14-20

If you want to use another comment character (like //) then change your command delimiter:

:14,20s!^!//!

Inserts a '//' character sequence at the start of lines 14-20

Or you can always just escape the // characters like:

:14,20s/^/\/\//

Inserts a '//' character sequence at the start of lines 14-20

If you are not seeing line numbers in your editor, simply type the following

:set nu

You probably mean the difference between Http Only cookies and their counter part?

Http Only cookies cannot be accessed (read from or written to) in client side JavaScript, only server side. If the Http Only flag is not set, or the cookie is created in (client side) JavaScript, the cookie can be read from and written to in (client side) JavaScript as well as server side.

1. Normal Class: A Java class

2. Java Beans:

   • All properties private (use getters/setters)
   • A public no-argument constructor
   • Implements Serializable.

3. Pojo: Plain Old Java Object is a Java object not bound by any restriction other than those forced by the Java Language Specification. I.e., a POJO should not have to

   • Extend prespecified classes
   • Implement prespecified interface
   • Contain prespecified annotations

You had two options.

Option 1: simplest makefile = NO MAKEFILE.

Rename "a3driver.cpp" to "a3a.cpp", and then on the command line write:

nmake a3a.exe

And that's it. If you're using GNU Make, use "make" or "gmake" or whatever.

Option 2: a 2-line makefile.

a3a.exe: a3driver.obj
    link /out:a3a.exe a3driver.obj

Short for Dimension. It's a type of variable. You declare (or "tell" Visual Basic) that you are setting up a variable with this word.

In many cases, continuing to scrape data from a website even when the server is requesting you not to is unethical. However, in the cases where it isn't, you can utilize a list of public proxies in order to scrape a website with many different IP addresses.

You could use the random.sample function from the standard library to select k elements from a population:

import random
random.sample(range(low, high), n)

In case of a rather large range of possible numbers, you could use itertools.islice with an infinite random generator:

import itertools
import random

def random_gen(low, high):
    while True:
        yield random.randrange(low, high)

gen = random_gen(1, 100)
items = list(itertools.islice(gen, 10))  # Take first 10 random elements

After the question update it is now clear that you need n distinct (unique) numbers.

import itertools
import random

def random_gen(low, high):
    while True:
        yield random.randrange(low, high)

gen = random_gen(1, 100)

items = set()

# Try to add elem to set until set length is less than 10
for x in itertools.takewhile(lambda x: len(items) < 10, gen):
    items.add(x)

If the string can not be converted to an integer, then

• int.Parse() will throw an exception
• int.TryParse() will return false (but not throw an exception)

Old question, but still first google hit, so i post it here so i find it again more easily...

Using Mongo 4.2 and an aggregate():

db.collection.aggregate(
    [
     { $match: { "end_time": { "$gt": ISODate("2020-01-01T00:00:00.000Z")  } } },
     { $project: {
          "end_day": { $dateFromParts: { 'year' : {$year:"$end_time"}, 'month' : {$month:"$end_time"}, 'day': {$dayOfMonth:"$end_time"}, 'hour' : 0  } }
     }}, 
     {$group:{
        _id:   "$end_day",
        "count":{$sum:1},
    }}
   ]
)

This one give you the groupby variable as a date, sometimes better to hande as the components itself.

LIKE 'WC[[]R]S123456' 

or

LIKE 'WC\[R]S123456' ESCAPE '\'

Should work.

Apparently, not only the absolute speeds but also the speed order (as reported by user1579844) are machine dependent; here's what I found:

a=np.empty(1e4); a.fill(5) is fastest;

In descending speed order:

timeit a=np.empty(1e4); a.fill(5) 
# 100000 loops, best of 3: 10.2 us per loop
timeit a=np.empty(1e4); a[:]=5
# 100000 loops, best of 3: 16.9 us per loop
timeit a=np.ones(1e4)*5
# 100000 loops, best of 3: 32.2 us per loop
timeit a=np.tile(5,[1e4])
# 10000 loops, best of 3: 90.9 us per loop
timeit a=np.repeat(5,(1e4))
# 10000 loops, best of 3: 98.3 us per loop
timeit a=np.array([5]*int(1e4))
# 1000 loops, best of 3: 1.69 ms per loop (slowest BY FAR!)

So, try and find out, and use what's fastest on your platform.

List<Person> roster = ...;

Map<String, Person> map = 
    roster
        .stream()
        .collect(
            Collectors.toMap(p -> p.getLast(), p -> p)
        );

that would be the translation, but i havent run this or used the API. most likely you can substitute p -> p, for Function.identity(). and statically import toMap(...)

The reason you see a difference between your implementation and Eclipse is because you scan each time, while Eclipse (and other tools) scan only once (during project load most of the times) and create an index. Next time you ask for the data it doesn't scan again, but look at the index.

I got this error after using $.getJSON() from JQuery. I just changed to post:

data = getDataObjectByForm(form);
var jqxhr = $.post(url, data, function(){}, 'json')
    .done(function (response) {
        if (response instanceof Object)
            var json = response;
        else
            var json = $.parseJSON(response);
        // console.log(response);
        // console.log(json);
        jsonToDom(json);
        if (json.reload != undefined && json.reload)
            location.reload();
        $("body").delay(1000).css("cursor", "default");
    })
    .fail(function (jqxhr, textStatus, error) {
        var err = textStatus + ", " + error;
        console.log("Request Failed: " + err);
        alert("Fehler!");
    });

I guess you have installed the 2.7 version manually, while 2.6 comes from a package?

The simple answer is: uninstall python package.

The more complex one is: do not install manually in /usr/local. Build a package with 2.7 version and then upgrade.

Package handling depends on what distribution you use.

Hi I'm also new to react and I also faced this problem after so many trouble I found solution: Just run in your command prompt or terminal :

npm config set registry http://registry.npmjs.org/

This will resolve your problem. Reference link: http://blog.csdn.net/zhalcie2011/article/details/78726679

Anything that is static is in the class level. You don't have to create instance to access static fields/method. Static variable will be created once when class is loaded.

Instance variables are the variable associated with the object which means that instance variables are created for each object you create. All objects will have separate copy of instance variable for themselves.

In your case, when you declared it as static final, that is only one copy of variable. If you change it from multiple instance, the same variable would be updated (however, you have final variable so it cannot be updated).

In second case, the final int a is also constant , however it is created every time you create an instance of the class where that variable is declared.

Have a look on this Java tutorial for better understanding ,

Try this code

.button:after {
    content: ""
    position: absolute
    width: 70px
    background-image: url('../../images/frontapp/mid-icon.svg')
    display: inline-block
    background-size: contain
    background-repeat: no-repeat
    right: 0
    bottom: 0
}

It's a class for Alert Dialog so that u can call the class from any activity to reuse the code.

public class MessageOkFragmentDialog extends DialogFragment {
Typeface Lato;
String message = " ";
String title = " ";
int messageID = 0;

public MessageOkFragmentDialog(String message, String title) {
    this.message = message;
    this.title = title;
}


@Override
public Dialog onCreateDialog(Bundle savedInstanceState) {

    AlertDialog.Builder builder = new AlertDialog.Builder(getActivity());

    LayoutInflater inflater = getActivity().getLayoutInflater();

    View convertview = inflater.inflate(R.layout.dialog_message_ok_box, null);


    Constants.overrideFonts(getActivity(), convertview);
    Lato = Typeface
            .createFromAsset(getActivity().getAssets(), "font/Lato-Regular.ttf");


    TextView textmessage = (TextView) convertview
            .findViewById(R.id.textView_dialog);

    TextView textview_dialog_title = (TextView) convertview.findViewById(R.id.textview_dialog_title);

    textmessage.setTypeface(Lato);
    textview_dialog_title.setTypeface(Lato);



    textmessage.setText(message);
    textview_dialog_title.setText(title);



    Button button_ok = (Button) convertview
            .findViewById(R.id.button_dialog);
    button_ok.setTypeface(Lato);

    builder.setView(convertview);
    button_ok.setOnClickListener(new OnClickListener() {
        @Override
        public void onClick(View arg0) {
            dismiss();

        }
    });


    return builder.create();

}
}

Xml file for the same is:

    <?xml version="1.0" encoding="utf-8"?>
<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
    android:layout_width="fill_parent"
    android:layout_height="match_parent"
    android:background="#ffffff"
    android:gravity="center_vertical|center"
    android:orientation="vertical">

    <LinearLayout
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:gravity="center"
        android:orientation="vertical">

        <LinearLayout
            android:layout_width="match_parent"
            android:layout_height="wrap_content"
            android:background="@color/blue_color"
            android:gravity="center_horizontal"
            android:orientation="horizontal">

            <TextView
                android:id="@+id/textview_dialog_title"
                android:layout_width="wrap_content"
                android:layout_height="50dp"
                android:gravity="center"
                android:textColor="@color/white_color"
                android:textSize="@dimen/txtSize_Medium" />


        </LinearLayout>

        <View
            android:layout_width="match_parent"
            android:layout_height="1dp"
            android:background="@color/txt_white_color" />

        <TextView
            android:id="@+id/textView_dialog"
            android:layout_width="wrap_content"
            android:layout_height="wrap_content"
            android:layout_gravity="center"
            android:layout_margin="@dimen/margin_20"
            android:textColor="@color/txt_gray_color"
            android:textSize="@dimen/txtSize_small" />

        <View
            android:layout_width="match_parent"
            android:layout_height="1dp"
            android:background="@color/txt_white_color"
            android:visibility="gone"/>

        <Button
            android:id="@+id/button_dialog"
            android:layout_width="wrap_content"
            android:layout_height="@dimen/margin_40"
            android:layout_gravity="center"
            android:background="@drawable/circular_blue_button"

            android:text="@string/ok"
            android:layout_marginTop="5dp"
            android:layout_marginBottom="@dimen/margin_10"
            android:textColor="@color/txt_white_color"
            android:textSize="@dimen/txtSize_small" />
    </LinearLayout>

</LinearLayout>

I had to do it once too for a homework. I followed this approach:

1. Define your data members in a struct.
2. Define your function members that take a pointer to your struct as first argument.
3. Do these in one header & one c. Header for struct definition & function declarations, c for implementations.

A simple example would be this:

/// Queue.h
struct Queue
{
    /// members
}
typedef struct Queue Queue;

void push(Queue* q, int element);
void pop(Queue* q);
// etc.
/// 

To really understand how HTTPS will increase your latency, you have to understand how HTTPS connections are established. Here is a nice diagram. The key is that instead of the client getting the data after 2 "legs" (one round trip, you send a request, the server sends a response), the client won't get data until at least 4 legs (2 round trips). So, if it takes 100 ms for a packet to move between the client and the server, your first HTTPS request will take at least 500 ms.

Of course, this can be mitigated by re-using the HTTPS connection (which browsers should do), but it does explain part of that initial stall when loading up an HTTPS web site.

Use an array for this.

var markers = [];
for (var i = 0; i < coords.length; ++i) {
    markers[i] = "some stuff";
}

I had this:

 class Util {
  static boolean isNeverAsync = System.getenv().get("asyncc_exclude_redundancy").equals("yes");
}

you can probably see the problem, the env var might return null instead of string. So just to test my theory, I changed it to:

 class Util {
  static boolean isNeverAsync = false;
}

and the problem went away. Too bad that Java can't give you the exact stack trace of the error though, kinda weird.

You can use the following as extension method

 public static void RemoveByValue<T,T1>(this Dictionary<T,T1> src , T1 Value)
    {
        foreach (var item in src.Where(kvp => kvp.Value.Equals( Value)).ToList())
        {
            src.Remove(item.Key);
        }
    }

That is the textarea's job - for multiline text input. The input won't do it; it wasn't designed to do it.

So use a textarea. Besides their visual differences, they are accessed via JavaScript the same way (use value property).

You can prevent newlines being entered via the input event and simply using a replace(/\n/g, '').

remote server> cd /home/ec2-user
remote server> git init --bare --shared  test
add ssh pub key to remote server
local> git remote add aws ssh://ec2-user@<hostorip>:/home/ec2-user/dev/test
local> git push aws master

I was having "(...) unable to handle this request. http error 500" and found out it was from a require_once that was working locally, on a windows machine, with backslash (\) as separator for directories but when i uploaded to my server it stopped working. I changed it to forward slash (/) and now is ok.

require_once ( 'cards\cards.php' ); // **http error 500**

require_once ( 'cards/cards.php' ); // OK

Im not sure if this is the answer you are expecting but, why don't you set the width of Tree to 'auto' and width of 'View' to 100% ?

For me it helped to enable the automated discovery in Properties -> C/C++-Build -> Discovery Options to resolve this problem.

Use RegexOptions.Singleline, it changes the meaning of . to include newlines

Regex.Replace(content, searchText, replaceText, RegexOptions.Singleline);

It's easy in VS2012; just use the change mapping feature:

1. Create the folder where you want the solution to be moved to.
2. Check-in all your project files (if you want to keep you changes), or rollback any checked out files.
3. Close the solution.
4. Open the Source Control Explorer.
5. Right-click the solution, and select "Advanced -> Remove Mapping..."
6. Change the "Local Folder" value to the one you created in step #1.
7. Select "Change".
8. Open the solution by double-clicking it in the source control explorer.

For windows users:

Download gradle binary from the link in the answer Gradle Download

Extract the zip file to 'C:\Gradle' or somewhere else

open Edit Environment variable dialog from start menu > Search

Click 'New' under system variables and add as below

Variable Name GRADLE_HOME Variable Value C:\Gradle\gradle-4.0.1

Then choose PATH variable from system variable list

append the gradle path to variable value like this C:\Gradle\gradle-4.0.1\bin

then press win Key+R type cmd then enter > in command terminal type gradle -v

if the setup is correct you will see the gradle installation details

Custom scroll bars aren't possible with CSS, you'll need some JavaScript magic.

Some browsers support non-spec CSS rules, such as ::-webkit-scrollbar in Webkit but is not ideal since it'll only work in Webkit. IE had something like that too, but I don't think they support it anymore.

For a quick single option solution, use if

If you only have a single option to check and it will always be the first option ($1) then the simplest solution is an if with a test ([). For example:

if [ "$1" == "-h" ] ; then
    echo "Usage: `basename $0` [-h]"
    exit 0
fi

Note that for posix compatibility = will work as well as ==.

Why quote $1?

The reason the $1 needs to be enclosed in quotes is that if there is no $1 then the shell will try to run if [ == "-h" ] and fail because == has only been given a single argument when it was expecting two:

$ [ == "-h" ]
bash: [: ==: unary operator expected

For anything more complex use getopt or getopts

As suggested by others, if you have more than a single simple option, or need your option to accept an argument, then you should definitely go for the extra complexity of using getopts.

As a quick reference, I like The 60 second getopts tutorial.<sup>†</sup>

You may also want to consider the getopt program instead of the built in shell getopts. It allows the use of long options, and options after non option arguments (e.g. foo a b c --verbose rather than just foo -v a b c). This Stackoverflow answer explains how to use GNU getopt.

<sup>† jeffbyrnes mentioned that the original link died but thankfully the way back machine had archived it.</sup>

Turns out you don't have to do much at all.

See below - the parameter x will contain the full HTTP body (which is XML in our case).

@POST
public Response go(String x) throws IOException {
    ...
}

If you give the address of online image in your django project it will work. that is working for me. You should take a shot.

• First in terminal make the script executable by typing the following command:

    chmod a+x yourscriptname
  

• Then, in Finder, right-click your file and select "Open with" and then "Other...".

• Here you select the application you want the file to execute into, in this case it would be Terminal. To be able to select terminal you need to switch from "Recommended Applications" to "All Applications". (The Terminal.app application can be found in the Utilities folder)

• NOTE that unless you don't want to associate all files with this extension to be run in terminal you should not have "Always Open With" checked.

• After clicking OK you should be able to execute you script by simply double-clicking it.

SELECCT TO_BASE64(blobfield)  
FROM the Table

worked for me.

The CAST(blobfield AS CHAR(10000) CHARACTER SET utf8) and CAST(blobfield AS CHAR(10000) CHARACTER SET utf16) did not show me the text value I wanted to get.

What I did is first check what are the running processes by

SELECT * FROM pg_stat_activity WHERE state = 'active';

Find the process you want to kill, then type:

SELECT pg_cancel_backend(<pid of the process>)

This basically "starts" a request to terminate gracefully, which may be satisfied after some time, though the query comes back immediately.

If the process cannot be killed, try:

SELECT pg_terminate_backend(<pid of the process>)

Whenever you want to extend the properties of User.Identity with any additional properties like the question above, add these properties to the ApplicationUser class first like so:

public class ApplicationUser : IdentityUser
{
    public async Task<ClaimsIdentity> GenerateUserIdentityAsync(UserManager<ApplicationUser> manager)
    {
        // Note the authenticationType must match the one defined in CookieAuthenticationOptions.AuthenticationType
        var userIdentity = await manager.CreateIdentityAsync(this, DefaultAuthenticationTypes.ApplicationCookie);
        // Add custom user claims here
        return userIdentity;
    }

    // Your Extended Properties
    public long? OrganizationId { get; set; }
}

Then what you need is to create an extension method like so (I create mine in an new Extensions folder):

namespace App.Extensions
{
    public static class IdentityExtensions
    {
        public static string GetOrganizationId(this IIdentity identity)
        {
            var claim = ((ClaimsIdentity)identity).FindFirst("OrganizationId");
            // Test for null to avoid issues during local testing
            return (claim != null) ? claim.Value : string.Empty;
        }
    }
}

When you create the Identity in the ApplicationUser class, just add the Claim -> OrganizationId like so:

    public async Task<ClaimsIdentity> GenerateUserIdentityAsync(UserManager<ApplicationUser> manager)
    {
        // Note the authenticationType must match the one defined in CookieAuthenticationOptions.AuthenticationType
        var userIdentity = await manager.CreateIdentityAsync(this, DefaultAuthenticationTypes.ApplicationCookie);
        // Add custom user claims here => this.OrganizationId is a value stored in database against the user
        userIdentity.AddClaim(new Claim("OrganizationId", this.OrganizationId.ToString()));

        return userIdentity;
    }

Once you added the claim and have your extension method in place, to make it available as a property on your User.Identity, add a using statement on the page/file you want to access it:

in my case: using App.Extensions; within a Controller and @using. App.Extensions withing a .cshtml View file.

EDIT:

What you can also do to avoid adding a using statement in every View is to go to the Views folder, and locate the Web.config file in there. Now look for the <namespaces> tag and add your extension namespace there like so:

<add namespace="App.Extensions" />

Save your file and you're done. Now every View will know of your extensions.

You can access the Extension Method:

var orgId = User.Identity.GetOrganizationId();

Simple copy paste instruction given here about .pem file

https://gist.github.com/luislavena/f064211759ee0f806c88

For certificate verification failed

If you've read the previous sections, you will know what this means (and shame > on you if you have not).

We need to download AddTrustExternalCARoot-2048.pem. Open a Command Prompt and type in:

C:>gem which rubygems C:/Ruby21/lib/ruby/2.1.0/rubygems.rb Now, let's locate that directory. From within the same window, enter the path part up to the file extension, but using backslashes instead:

C:>start C:\Ruby21\lib\ruby\2.1.0\rubygems This will open a Explorer window inside the directory we indicated.

Step 3: Copy new trust certificate

Now, locate ssl_certs directory and copy the .pem file we obtained from previous step inside.

It will be listed with other files like GeoTrustGlobalCA.pem.

$var = 'abcdef';
if(isset($var))
{
   if (strlen($var) > 0);
   {
      //do something, string length greater than zero
   }
   else
   {
     //do something else, string length 0 or less
   }
}

This is a simple example. Hope it helps.

edit: added isset in the event a variable isn't defined like above, it would cause an error, checking to see if its first set at the least will help remove some headache down the road.

Presumably this would work:

IF(compliment = 'set' OR compliment = 'Y' OR compliment = 1, 'Y', 'N') AS customer_compliment

Looking at Sublime Text Unofficial Documentation's article on Search and Replace, it looks like +(.+) is the capture group you might want... but I personally used (.*) and it worked well. To REPLACE in the way you are saying, you might like this conversation in the forums, specifically this post which says to simply use $1 to use the first captured group.

And since pictures are better than words...

Before:

After:

The DPI of the screen of the Nexus 10 is ±300, which is in the unofficial xhdpi range of 280-400.

Usually, devices use resources designed for their density. But there are exceptions, and exceptions might be added in the future. The Nexus 10 uses xxhdpi resources when it comes to launcher icons.

The standard quantised DPI for xxhdpi is 480 (which means screens with a DPI somewhere in the range of 400-560 are probably xxhdpi).

Make the class serializable by implementing the interface java.io.Serializable.

• java.io.Serializable - Marker Interface which does not have any methods in it.
• Purpose of Marker Interface - to tell the ObjectOutputStream that this object is a serializable object.

SWIFT 3 Example

override func viewDidLoad() {

    self.backgroundImageView.addGestureRecognizer(
        UITapGestureRecognizer.init(target: self, action:#selector(didTapImageview(_:)))
    )

    self.backgroundImageView.isUserInteractionEnabled = true
}

func didTapImageview(_ sender: Any) {
    // do something
}

No gesture recongnizer delegates or other implementations where necessary.

Your ngClick is correct; you just need the right service. $location is what you're looking for. Check out the docs for the full details, but the solution to your specific question is this:

$location.path( '/new-page.html' );

The $location service will add the hash (#) if it's appropriate based on your current settings and ensure no page reload occurs.

You could also do something more flexible with a directive if you so chose:

.directive( 'goClick', function ( $location ) {
  return function ( scope, element, attrs ) {
    var path;

    attrs.$observe( 'goClick', function (val) {
      path = val;
    });

    element.bind( 'click', function () {
      scope.$apply( function () {
        $location.path( path );
      });
    });
  };
});

And then you could use it on anything:

<button go-click="/go/to/this">Click!</button>

There are many ways to improve this directive; it's merely to show what could be done. Here's a Plunker demonstrating it in action: http://plnkr.co/edit/870E3psx7NhsvJ4mNcd2?p=preview.

use IDLE Editor {You may already have it} it has interactive shell for python and it will show you execution and result.