[python] Python Pandas Counting the Occurrences of a Specific value

I am trying to find the number of times a certain value appears in one column.

I have made the dataframe with data = pd.DataFrame.from_csv('data/DataSet2.csv')

and now I want to find the number of times something appears in a column. How is this done?

I thought it was the below, where I am looking in the education column and counting the number of time ? occurs.

The code below shows that I am trying to find the number of times 9th appears and the error is what I am getting when I run the code

Code

missing2 = df.education.value_counts()['9th']
print(missing2)

Error

KeyError: '9th'

easy but not efficient:

list(df.education).count('9th')

for finding a specific value of a column you can use the code below

irrespective of the preference you can use the any of the method you like

df.col_name.value_counts().Value_you_are_looking_for

take example of the titanic dataset

df.Sex.value_counts().male

this gives a count of all male on the ship Although if you want to count a numerical data then you cannot use the above method because value_counts() is used only with series type of data hence fails So for that you can use the second method example

the second method is

#this is an example method of counting on a data frame
df[(df['Survived']==1)&(df['Sex']=='male')].counts()

this is not that efficient as value_counts() but surely will help if you want to count values of a data frame hope this helps

Couple of ways using count or sum

In [338]: df
Out[338]:
  col1 education
0    a       9th
1    b       9th
2    c       8th

In [335]: df.loc[df.education == '9th', 'education'].count()
Out[335]: 2

In [336]: (df.education == '9th').sum()
Out[336]: 2

In [337]: df.query('education == "9th"').education.count()
Out[337]: 2

An elegant way to count the occurrence of '?' or any symbol in any column, is to use built-in function isin of a dataframe object.

Suppose that we have loaded the 'Automobile' dataset into df object. We do not know which columns contain missing value ('?' symbol), so let do:

df.isin(['?']).sum(axis=0)

DataFrame.isin(values) official document says:

it returns boolean DataFrame showing whether each element in the DataFrame is contained in values

Note that isin accepts an iterable as input, thus we need to pass a list containing the target symbol to this function. df.isin(['?']) will return a boolean dataframe as follows.

    symboling   normalized-losses   make    fuel-type   aspiration-ratio ...
0   False       True                False   False       False
1   False       True                False   False       False
2   False       True                False   False       False
3   False       False               False   False       False
4   False       False               False   False       False
5   False       True                False   False       False
...

To count the number of occurrence of the target symbol in each column, let's take sum over all the rows of the above dataframe by indicating axis=0. The final (truncated) result shows what we expect:

symboling             0
normalized-losses    41
...
bore                  4
stroke                4
compression-ratio     0
horsepower            2
peak-rpm              2
city-mpg              0
highway-mpg           0
price                 4

Try this:

(df[education]=='9th').sum()