Pandas DataFrame: GroupBy Examples

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Table of Contents

For Dataframe usage examples not related to GroupBy, see Pandas Dataframe by Example

View all examples in this post here: jupyter notebook: pandas-groupby-post

Concatenate strings in group

This is called GROUP_CONCAT in databases such as MySQL. See below for more exmaples using the apply() function.

In the original dataframe, each row is a tag assignment.

import pandas as pd

df = pd.DataFrame({
    'user_id':[1,2,1,3,3,],
    'content_id':[1,1,2,2,2],
    'tag':['cool','nice','clever','clever','not-bad']
})

df.groupby("content_id")['tag'].apply(lambda tags: ','.join(tags))

After the operation, we have one row per content_id and all tags are joined with ','.

source-dataframe Source dataframe
    
tags-by-content All tags given to each content

Number of unique values per group

How many unique users have tagged each movie?

import pandas as pd

df = pd.DataFrame({
    'user_id':[1,2,1,3,3,],
    'content_id':[1,1,2,2,2],
    'tag':['cool','nice','clever','clever','not-bad']
})

df.groupby("container_id")["user_id"].nunique().to_frame()

source-dataframe Source dataframe
      
tags-by-content How many users tagged each content?

Sort groupby results

Turn the GroupBy object into a regular dataframe by calling .to_frame() and then reindex with reset_index(), then you call sort_values() as you would a normal DataFrame:

import pandas as pd

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

df1 = df.groupby('product')['value'].sum().to_frame().reset_index()

df2 = df.groupby('product')['value'].sum().to_frame().reset_index().sort_values(by='value')

original-dataframe Original Dataframe
      
dataframe-1 Total value for each product:
df1 has the default ordering

dataframe-2 Total value for each product:
df2 has been ordered by value, ascending

GroupBy Plot Group Size

For many more examples on how to plot data directly from Pandas see: Pandas Dataframe: Plot Examples with Matplotlib and Pyplot

If you have matplotlib installed, you can call .plot() directly on the output of methods on GroupBy objects, such as sum(), size(), etc.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

plt.clf()
df.groupby('product').size().plot(kind='bar')
plt.show()

Original dataframe Original dataframe
      
groupby-dataframe-size Plot: Number of records by product

GroupBy Plot Sum

As above

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

plt.clf()
df.groupby('product').sum().plot(kind='bar')
plt.show()

Original dataframe Original dataframe
      
groupby-dataframe-size Plot: Sum of column value by product.
Pandas automatically sets axes and legends too

GroupBy Plot Mean with error bars

Add error bars (mean +/- the standard deviation1) to help people understand whether they can trust the averages or whether variance is too high:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'purchase_price':[28.45, 25.89,32.12,99.99,120.00,170.00,12.22,28.22,5.00],
    'product':['table','table','table','mobile phone','mobile phone','mobile phone','chair','chair','chair']
})

plt.clf()

ax = plt.gca()

# plot the means
df.groupby('product').mean().plot(kind='bar',color='lightblue',ax=ax)

# generate a dataframe with means and standard deviations
grouped_df=df.groupby('product').agg([np.mean,np.std])

# flatten column names
grouped_df.columns = [col for col in v.columns.values]

# iterrows is usually very slow but since this is a grouped
# dataframe, there wonly be many rows
for i,(index,row) in enumerate(grouped_df.iterrows()):
    name = row.name
    mean = row['mean']
    stddev = row['std']

    # plot the vertical line for this group
    ax.vlines(x=i,ymin=mean-stddev,ymax=mean+stddev)

plt.show()

source-dataframe Original Dataframe
         
average-plot-with-error-bars The average purchase price for each product.
The black bars shows how different, on average, prices are
from the average in that group.
This helps people understand if the average can
be trusted as a good summary of the data.

Multiple aggregation operations, single GroupBy pass

Say, for instance, ORDER_DATE is a timestamp column. We want to find out the total quantity QTY AND the average UNIT price per day.

Note: we're not using the sample dataframe here

grouped = df.groupby(p7.ORDER_DATE.dt.day).agg({
    "QTY":np.sum,
    "UNIT": np.mean
})

Flatten hierarchical indices

It's useful to execute multiple aggregations in a single pass using the DataFrameGroupBy.agg() method (see above).

But the result is a dataframe with hierarchical columns, which are not very easy to work with.

You can flatten multiple aggregations on a single columns using the following procedure:

import pandas as pd

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

# call
grouped_df = df.groupby('product').agg({'value':['min','max','mean']})

At this point, join together the columns, with '_' in between and the reset the index:

grouped_df.columns = ['_'.join(col) for col in grouped_df.columns.values]
grouped_df = grouped_df.reset_index()

original-dataframe Original Dataframe
      
dataframe-1 The agg() method creates a hierarchical index
dataframe-2 Much better: Join hierarchical columns together
using '_' and then call reset_index()
for a simpler index structure

Iterate over groups

To iterate over dataframe groups in groupby(), the object returned by the call itself can be used as an iterator:

for key,group_df in df.groupby('product'):

    # `key` contains the name of the grouped element 
    # i.e. 'chair', 'mobile phone', 'table'

    # `group_df` is a normal dataframe
    # containing only the data referring to the key

    print("the group for product '{}' has {} rows".format(key,len(group_df)))  

# >>>
# the group for product 'chair' has 2 rows
# the group for product 'mobile phone' has 2 rows
# the group for product 'table' has 3 rows

For group in groups

See above: Iterate over dataframe groups

Change aggregation column name

By default, aggregation columns get the name of the column being aggregated over, in this case value

import pandas as pd

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

grouped_df = df.groupby('product')['value'].sum().reset_index(name='value_sum')

original-dataframe Original Dataframe
      
dataframe-1 By default, aggregation columns get the name of the
column being aggregated over, in this case value

dataframe-2 Give it a more intuitive name using reset_index(name='new name')

Get group by key

After calling groupby(), you can access each group dataframe individually using get_group().

import pandas as pd

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

# grouped_df is a DataFrameGroupBy containing each individual group as a dataframe
grouped_df = df.groupby('product')

# you get can a dataframe containing the values for a single group
# using .get_group('group_key')
grouped_df.get_group('chair')

Original dataframe Original dataframe
      
groupby-apply-tolist Results of .get_group('chair'): only
results in 'chair' group are returned

List values in group

See below for more examples using the apply() function.

Use apply with a custom function.

For example, get a list of the prices for each product:

import pandas as pd

df = pd.DataFrame({
    'value':[20.45,22.89,32.12,111.22,33.22,100.00,99.99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

# note that the apply function here takes a series made up of the values
# for each group. We then call the .tolist() method on the series to make 
# it into a list
df.groupby('product')['value'].apply(lambda group_series: group_series.tolist()).reset_index()

Original dataframe Original dataframe
      
groupby-apply-tolist Group values for each group into a list

Custom aggregation

Use apply(func) where func is a function that takes a Series representing a single group and reduces that Series to a single value.

Example: get count of even values in each group

import pandas as pd

df = pd.DataFrame({
    'value':[20,22,32,111,33,100,99],
    'product':['table','chair','chair','mobile phone','table','mobile phone','table']
})

# you can define a function like this or use a lambda function
def count_even_numbers(series):
  return len([elem for elem in series if elem % 2 == 0 ])

df.groupby('product')['value'].apply(count_even_numbers).reset_index(name='num_even_numbers')

Original dataframe Original dataframe
      
groupby-apply-tolist How many even-numbered values in each group?

Sample rows after groupby

This is also called stratified sampling.

The easiest way to do this is df.groupby(<colname>).apply:

import pandas as pd

df = pd.DataFrame({
  'price':[20,22,32,111,33,100,99],
  'product':['table','chair','chair','table','table','chair','table']
})

# you could just as easily group by multiple columns here
df.groupby("product").apply(
  lambda group_df: group_df.sample(2) # any dataframe function could be used here
).reset_index(drop=True) # fixes the index

source-dataframe Source dataframe with chairs and tables
         
stratified-sample-dataframe Sampled dataframe, with 2 samples
per group.
Your results may vary because
this is a random sample.


1: This is actually the standard error; this is the name given to the sample standard deviation.

Dialogue & Discussion