Summarising, Aggregating, and Grouping data in Python Pandas


Pandas – Python Data Analysis Library

I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.

One aspect that I’ve recently been exploring is the task of grouping large data frames by different variables, and applying summary functions on each group. This is accomplished in Pandas using the “groupby()” and “agg()” functions of Panda’s DataFrame objects.

Update: Pandas version 0.20.1 in May 2017 changed the aggregation and grouping APIs. This post has been updated to reflect the new changes.

A Sample DataFrame

Download File IconIn order to demonstrate the effectiveness and simplicity of the grouping commands, we will need some data. For an example dataset, I have extracted my own mobile phone usage records. I analyse this type of data using Pandas during my work on KillBiller. If you’d like to follow along – the full csv file is available here.

The dataset contains 830 entries from my mobile phone log spanning a total time of 5 months. The CSV file can be loaded into a pandas DataFrame using the pandas.DataFrame.from_csv() function, and looks like this:

 

indexdatedurationitemmonthnetworknetwork_type
015/10/14 06:5834.429data2014-11datadata
115/10/14 06:5813.000call2014-11Vodafonemobile
215/10/14 14:4623.000call2014-11Meteormobile
315/10/14 14:484.000call2014-11Tescomobile
415/10/14 17:274.000call2014-11Tescomobile
515/10/14 18:554.000call2014-11Tescomobile
616/10/14 06:5834.429data2014-11datadata
716/10/14 15:01602.000call2014-11Threemobile
816/10/14 15:121050.000call2014-11Threemobile
916/10/14 15:3019.000call2014-11voicemailvoicemail
1016/10/14 16:211183.000call2014-11Threemobile
1116/10/14 22:181.000sms2014-11Meteormobile

The main columns in the file are:

  1. date: The date and time of the entry
  2. duration: The duration (in seconds) for each call, the amount of data (in MB) for each data entry, and the number of texts sent (usually 1) for each sms entry.
  3. item: A description of the event occurring – can be one of call, sms, or data.
  4. month: The billing month that each entry belongs to – of form ‘YYYY-MM’.
  5. network: The mobile network that was called/texted for each entry.
  6. network_type: Whether the number being called was a mobile, international (‘world’), voicemail, landline, or other (‘special’) number.

Phone numbers were removed for privacy. The date column can be parsed using the extremely handy dateutil library.

import pandas as pd
import dateutil

# Load data from csv file
data = pd.DataFrame.from_csv('phone_data.csv')
# Convert date from string to date times
data['date'] = data['date'].apply(dateutil.parser.parse, dayfirst=True)

Summarising the DataFrame

Once the data has been loaded into Python, Pandas makes the calculation of different statistics very simple. For example, mean, max, min, standard deviations and more for columns are easily calculable:

# How many rows the dataset
data['item'].count()
Out[38]: 830

# What was the longest phone call / data entry?
data['duration'].max()
Out[39]: 10528.0

# How many seconds of phone calls are recorded in total?
data['duration'][data['item'] == 'call'].sum()
Out[40]: 92321.0

# How many entries are there for each month?
data['month'].value_counts()
Out[41]: 
2014-11    230
2015-01    205
2014-12    157
2015-02    137
2015-03    101
dtype: int64

# Number of non-null unique network entries
data['network'].nunique()
Out[42]: 9

The need for custom functions is minimal unless you have very specific requirements. The full range of basic statistics that are quickly calculable and built into the base Pandas package are:

FunctionDescription
countNumber of non-null observations
sumSum of values
meanMean of values
madMean absolute deviation
medianArithmetic median of values
minMinimum
maxMaximum
modeMode
absAbsolute Value
prodProduct of values
stdUnbiased standard deviation
varUnbiased variance
semUnbiased standard error of the mean
skewUnbiased skewness (3rd moment)
kurtUnbiased kurtosis (4th moment)
quantileSample quantile (value at %)
cumsumCumulative sum
cumprodCumulative product
cummaxCumulative maximum
cumminCumulative minimum

The .describe() function is a useful summarisation tool that will quickly display statistics for any variable or group it is applied to. The describe() output varies depending on whether you apply it to a numeric or character column.

Summarising Groups in the DataFrame

There’s further power put into your hands by mastering the Pandas “groupby()” functionality. Groupby essentially splits the data into different groups depending on a variable of your choice. For example, the expression data.groupby(‘month’)  will split our current DataFrame by month.

The groupby() function returns a GroupBy object, but essentially describes how the rows of the original data set has been split. the GroupBy object .groups variable is a dictionary whose keys are the computed unique groups and corresponding values being the axis labels belonging to each group. For example:

data.groupby(['month']).groups.keys()
Out[59]: ['2014-12', '2014-11', '2015-02', '2015-03', '2015-01']

len(data.groupby(['month']).groups['2014-11'])
Out[61]: 230

Functions like max(), min(), mean(), first(), last() can be quickly applied to the GroupBy object to obtain summary statistics for each group – an immensely useful function. This functionality is similar to the dplyr and plyr libraries for R. Different variables can be excluded / included from each summary requirement.

# Get the first entry for each month
data.groupby('month').first()
Out[69]: 
                       date  duration  item   network network_type
month                                                             
2014-11 2014-10-15 06:58:00    34.429  data      data         data
2014-12 2014-11-13 06:58:00    34.429  data      data         data
2015-01 2014-12-13 06:58:00    34.429  data      data         data
2015-02 2015-01-13 06:58:00    34.429  data      data         data
2015-03 2015-02-12 20:15:00    69.000  call  landline     landline

# Get the sum of the durations per month
data.groupby('month')['duration'].sum()
Out[70]: 
month
2014-11    26639.441
2014-12    14641.870
2015-01    18223.299
2015-02    15522.299
2015-03    22750.441
Name: duration, dtype: float64

# Get the number of dates / entries in each month
data.groupby('month')['date'].count()
Out[74]: 
month
2014-11    230
2014-12    157
2015-01    205
2015-02    137
2015-03    101
Name: date, dtype: int64

# What is the sum of durations, for calls only, to each network
data[data['item'] == 'call'].groupby('network')['duration'].sum()
Out[78]: 
network
Meteor 7200
Tesco 13828
Three 36464
Vodafone 14621
landline 18433
voicemail 1775
Name: duration, dtype: float64

You can also group by more than one variable, allowing more complex queries.

# How many calls, sms, and data entries are in each month?
data.groupby(['month', 'item'])['date'].count()
Out[76]: 
month    item
2014-11  call    107
         data     29
         sms      94
2014-12  call     79
         data     30
         sms      48
2015-01  call     88
         data     31
         sms      86
2015-02  call     67
         data     31
         sms      39
2015-03  call     47
         data     29
         sms      25
Name: date, dtype: int64

# How many calls, texts, and data are sent per month, split by network_type?
data.groupby(['month', 'network_type'])['date'].count()
Out[82]: 
month network_type
2014-11 data 29
 landline 5
 mobile 189
 special 1
 voicemail 6
2014-12 data 30
 landline 7
 mobile 108
 voicemail 8
 world 4
2015-01 data 31
 landline 11
 mobile 160
....

Groupby output format – Series or DataFrame?

The output from a groupby and aggregation operation varies between Pandas Series and Pandas Dataframes, which can be confusing for new users. As a rule of thumb, if you calculate more than one column of results, your result will be a Dataframe. For a single column of results, the agg function, by default, will produce a Series.

You can change this by selecting your operation column differently:

data.groupby('month')['duration'].sum() # produces Pandas Series
data.groupby('month')[['duration']].sum() # Produces Pandas DataFrame

The groupby output will have an index or multi-index on rows corresponding to your chosen grouping variables. To avoid setting this index, pass “as_index=False” to the groupby operation.

data.groupby('month', as_index=False).agg({"duration": "sum"})
Using the as_index parameter while Grouping data in pandas prevents setting a row index on the result.

Multiple Statistics per Group

The final piece of syntax that we’ll examine is the “agg()” function for Pandas. The aggregation functionality provided by the agg() function allows multiple statistics to be calculated per group in one calculation. The syntax is simple, and is similar to that of MongoDB’s aggregation framework.

There were substantial changes to the Pandas aggregation function in May of 2017. Renaming of variables within the agg() function no longer functions as in the diagram below – see notes.

Aggregating statistics for multiple columns in pandas with groupby
Aggregation of variables in a Pandas Dataframe using the agg() function. Note that in Pandas versions 0.20.1 onwards, the renaming of results needs to be done separately.

 

Applying a single function to columns in groups

Instructions for aggregation are provided in the form of a python dictionary or list. The dictionary keys are used to specify the columns upon which you’d like to perform operations, and the dictionary values to specify the function to run.

For example:

# Group the data frame by month and item and extract a number of stats from each group
data.groupby(['month', 'item']).agg({'duration':sum,      # find the sum of the durations for each group
                                     'network_type': "count", # find the number of network type entries
                                     'date': 'first'})    # get the first date per group

The aggregation dictionary syntax is flexible and can be defined before the operation. You can also define functions inline using “lambda” functions to extract statistics that are not provided by the built-in options.

# Define the aggregation procedure outside of the groupby operation
aggregations = {
    'duration':'sum',
    'date': lambda x: max(x) - 1
}
data.groupby('month').agg(aggregations)

Applying multiple functions to columns in groups

To apply multiple functions to a single column in your grouped data, expand the syntax above to pass in a list of functions as the value in your aggregation dataframe. See below:

# Group the data frame by month and item and extract a number of stats from each group
data.groupby(['month', 'item']).agg({'duration': [min, max, sum],      # find the min, max, and sum of the duration column
                                     'network_type': "count", # find the number of network type entries
                                     'date': [min, 'first', 'nunique']})    # get the min, first, and number of unique dates per group

The agg(..) syntax is flexible and simple to use. Remember that you can pass in custom and lambda functions to your list of aggregated calculations, and each will be passed the values from the column in your grouped data.

Renaming grouped statistics from groupby operations

When multiple statistics are calculated on columns, the resulting dataframe will have a multi-index set on the column axis. This can be difficult to work with, and I typically have to rename columns after a groupby operation.

One option is to drop the top level (using .droplevel) of the newly created multi-index on columns using:

grouped = data.groupby('month').agg("duration": [min, max, mean])
grouped.columns = grouped.columns.droplevel(level=0)
grouped.rename(columns={"min": "min_duration", "max": "max_duration", "mean": "mean_duration"})
grouped.head()

However, this approach loses the original column names, leaving only the function names as column headers. A neater approach, as suggested to me by a reader, is using the ravel() method on the grouped columns. Ravel() turns a Pandas multi-index into a simpler array, which we can combine into sensible column names:

grouped = data.groupby('month').agg("duration": [min, max, mean]) 
# Using ravel, and a string join, we can create better names for the columns:
grouped.columns = ["_".join(x) for x in grouped.columns.ravel()]
Quick renaming of grouped columns from the groupby() multi-index can be achieved using the ravel() function.

<DEPRECATED> Dictionary groupby format

In older Pandas releases (< 0.20.1), rename the newly calculated columns was possible through nested dictionaries, or by passing a list of functions for a column. Our final example calculates multiple values from the duration column and names the results appropriately. Note that the results have multi-indexed column headers.

Note this syntax will no longer work for new installations of Python Pandas. 

# Define the aggregation calculations
aggregations = {
    'duration': { # work on the "duration" column
        'total_duration': 'sum',  # get the sum, and call this result 'total_duration'
        'average_duration': 'mean', # get mean, call result 'average_duration'
        'num_calls': 'count'
    },
    'date': {     # Now work on the "date" column
        'max_date': 'max',   # Find the max, call the result "max_date"
        'min_date': 'min',
        'num_days': lambda x: max(x) - min(x)  # Calculate the date range per group
    },
  &nbsp; 'network': ["count", "max"]  # Calculate two results for the 'network' column with a list
}

# Perform groupby aggregation by "month", but only on the rows that are of type "call"
data[data['item'] == 'call'].groupby('month').agg(aggregations)

Aggregation and summarisation of data using pandas python on mobile phone data

Wrap up

The groupby functionality in Pandas is well documented in the official docs and performs at speeds on a par (unless you have massive data and are picky with your milliseconds) with R’s data.table and dplyr libraries.

If you are interested in another example for practice, I used these same techniques to analyse weather data for this post, and I’ve put “how-to” instructions here.

There are plenty of resources online on this functionality, and I’d recommomend really conquering this syntax if you’re using Pandas in earnest at any point.

  • DataQuest Tutorial on Data Analysis: https://www.dataquest.io/blog/pandas-tutorial-python-2/
  • Chris Albon notes on Groups: https://chrisalbon.com/python/pandas_apply_operations_to_groups.html
  • Greg Reda Pandas Tutorial: http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/

72 thoughts on “Summarising, Aggregating, and Grouping data in Python Pandas”

  1. Pingback: Wunderground Data With Python Pandas & Seaborn | Shane Lynn

  2. Great post ! Learned so much from your blog. Thanks.

    BTW: How to draw a table like you did in out[47] ? It looks nice !
    Thank you !

    1. Hi Chandler, thanks for writing in, great to see that people are reading the blog and finding it interesting.

      The output in the diagram is the default layout for Pandas Dataframes when you print them in a “Jupyter notebook”. If you’re not already using notebooks – I would recommend having a look as a great tool to share analysis results and explore things. You should find the install instructions at http://jupyter.org and they also come installed with the “Anaconda” python distribution. https://www.continuum.io/downloads

  3. This is amazing, thank you! One question, once you’ve grouped and aggregated data, how do you select it and filter on it? For example, in your last example, you have a column for count. How would you limit the data in your df to only include counts of above or below a certain number? This is easy for me in SQL, but I haven’t been able to understand how to do this in Pandas.

    Thanks!

    1. Hi Ashley, thanks for the feedback. Filtering in Pandas is pretty easy, I tend to go with logical vectors to filter the data frame. So for example, to filter a data frame “df” on the “count” variable, you can use df[df[‘count’] > 5] or df[df[‘count’] == 10], or you can specify the index separately:
      idx = (df[‘count’] > 1) & (df[‘count’] < 10) # get index where count is between 1 and 10. df = df[idx] # Filter the actual data frame.

  4. Pingback: Pandas Aggregation | G. Wu

  5. Hi , I tried using the agg() to get mean,std and max for a column in DF , but it gives me an error ‘Series object has no attribute ‘agg’ . Could you help me with that.

    1. Hey Am, sounds like you are trying to apply the .agg() function to a pandas Series rather than a DataFrame – have a look at the datatype to check before you run the code.

  6. Thanks – very helpful – please note typo in the first code block

    data = pd.DataFrame.from_csv(‘phone_data.cv’) you have cv instead of csv – took me a few runs to figure it out!

    1. Great spot Trevor, I’ll fix that up now! Glad you found the post useful, and good luck with your python and pandas learning.

  7. Excellent blog. Thanks a lot for taking time to put this together and in the process helping many people like me understand Pandas better.
    There is lot of material out there on Pandas but I think this is one of the best in terms of explaining stuff with excellent example and clarity.
    Great Job!

    1. Thanks Chala, glad that you found it useful. I think it fits well with the pandas stuff out there – but perhaps with a slant towards a data-science user, which is how I use pandas!

  8. Very nice write-up. Do you know how to preserve the order of the aggregated columns? They do not show up in the same order as give in the aggregators object.

    1. Great question, and I don’t know the answer – the columns in the results do appear to be relatively randomly ordered. There appears to be a relationship with whether you have “sub-queries” to the order in the pandas output, but I think you may just have to order them yourself afterwards if order is important.

  9. Pingback: Select and Index with iloc and loc in Pandas | Shane Lynn

  10. Thank you so much for this post! You’ve solved the problem I’d been struggling with for ages due to a misunderstanding about pandas operations. I can finally move on with my project!

  11. Thanks for a great post, really useful!
    I just had one problem reproducing your last block on defining the aggregation calculation and renaming columns – specifically

    ‘num_days’: lambda x: max(x) – min(x) # Calculate the date range per group

    returned an error

    TypeError: unsupported operand type(s) for -: ‘str’ and ‘str’

    I am using python 3.5 – dont know if that makes a difference?

    1. Hey Michael, sounds like the data type for the column “num_days” in your data frame is being loaded as a string. Have a look using data[‘num_days’].dtype and ensure that it’s an integer/float before you run the code. There may be something up with the CSV input data in this case.

  12. Great Blog !!! Very helful.
    I have a question .. please help me to clarify.

    I have to add a new column in my panda dataframe and needs to copy the records in new column from the other column of same dataframe on condition like df.groupby([id]).first().

    I know how to do it in sas but not sure how to do it in pandas:

    In sas i can do something like this:

    data df;
    retain col_1″ “;
    set df1;
    by id;
    if first.id then col_1=col_2;
    else col_1=col_1;
    run;

  13. Nice and helpful!
    I’m comparing this to SQL and have 1 question:
    How about finding a sum for only 2 longest calls every moth?
    Can we use groupby(), agg() and rank() together in one go?
    For example, can we use data.groupby(‘month’) and then use [‘duration’].rank(ascending=False)<=2 to find only the 2 longest call durations for every month, and then also agg() those 2 call durations for every month?

  14. Thank you for sharing. The post is very helpful.
    I have one question here. In aggregations, how to get quantile .05, .25? I used quantile(.05), but always got error below:
    ‘SeriesGroupBy’ object has no attribute ‘quantile(.05)

    Can someone help to advise the solution?

    1. Hi DongMei, I think you can work out the quantile using a lambda function. Try something like:
      data.groupby("group_column").agg({"work_column": lambda x: x.quantile(0.05)})

      For two columns in the output, you can drop a level from the column headers using:
      data.columns = data.columns.droplevel(level=0)

      Be careful of your naming conventions though if you do this.

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