Dataframe.resample() works only with timeseries data. I cannot find a way of getting every nth row from non-timeseries data. What is the best method?

This question is related to
`python`

`pandas`

`resampling`

A solution I came up with when using the index was not viable ( possibly the multi-Gig .csv was too large, or I missed some technique that would allow me to reindex without crashing ).

Walk through one row at a time and add the nth row to a new dataframe.

```
import pandas as pd
from csv import DictReader
def make_downsampled_df(filename, interval):
with open(filename, 'r') as read_obj:
csv_dict_reader = DictReader(read_obj)
column_names = csv_dict_reader.fieldnames
df = pd.DataFrame(columns=column_names)
for index, row in enumerate(csv_dict_reader):
if index % interval == 0:
print(str(row))
df = df.append(row, ignore_index=True)
return df
```

```
df.drop(labels=df[df.index % 3 != 0].index, axis=0) # every 3rd row (mod 3)
```

There is an even simpler solution to the accepted answer that involves directly invoking `df.__getitem__`

.

```
df = pd.DataFrame('x', index=range(5), columns=list('abc'))
df
a b c
0 x x x
1 x x x
2 x x x
3 x x x
4 x x x
```

For example, to get every 2 rows, you can do

```
df[::2]
a b c
0 x x x
2 x x x
4 x x x
```

There's also `GroupBy.first`

/`GroupBy.head`

, you group on the index:

```
df.index // 2
# Int64Index([0, 0, 1, 1, 2], dtype='int64')
df.groupby(df.index // 2).first()
# Alternatively,
# df.groupby(df.index // 2).head(1)
a b c
0 x x x
1 x x x
2 x x x
```

The index is floor-divved by the stride (2, in this case). If the index is non-numeric, instead do

```
# df.groupby(np.arange(len(df)) // 2).first()
df.groupby(pd.RangeIndex(len(df)) // 2).first()
a b c
0 x x x
1 x x x
2 x x x
```

I had a similar requirement, but I wanted the n'th item in a particular group. This is how I solved it.

```
groups = data.groupby(['group_key'])
selection = groups['index_col'].apply(lambda x: x % 3 == 0)
subset = data[selection]
```

Though @chrisb's accepted answer does answer the question, I would like to add to it the following.

A simple method I use to get the `nth`

data or drop the `nth`

row is the following:

```
df1 = df[df.index % 3 != 0] # Excludes every 3rd row starting from 0
df2 = df[df.index % 3 == 0] # Selects every 3rd raw starting from 0
```

This arithmetic based sampling has the ability to enable even more complex row-selections.

This **assumes**, of course, that you have an `index`

column of *ordered, consecutive, integers* starting at 0.

Source: Stackoverflow.com