The querier is a query language for Python pandas Data Frames, inspired by relational databases querying. If you like SQL, Structured Query Language, you’ll like the querier. If you haven’t had a taste of SQL yet, no problem: the querier’s language is intuitive, and contains 9 verbs in its current form. You can see how these verbs work individually in the following notebooks:

  • concat: concatenates two Data Frames, either horizontally or vertically
  • delete: deletes rows from a Data Frame based on given criteria
  • drop: drops columns from a Data Frame
  • filtr: filters rows of the Data Frame based on given criteria
  • join: joins two Data Frames based on given criteria
  • select: selects columns from the Data Frame
  • summarize: obtains summaries of data based on grouping columns
  • update: updates a column, using an operation given by the user
  • request: for operations more complex than the previous 8 ones, makes it possible to use a SQL query on the Data Frame

It is now possible to compose the querier’s verbs, to construct more powerful queries for your Data Frames. Here is how to do it:

Installing the package

From command line:

!pip install git+

Import packages and dataset

import pandas as pd
import querier as qr

# Import data -----

url = (''
df = pd.read_csv(url)
total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4


  • select columns tip, sex, smoker, time from tips dataset
  • filter rows in which smoker == No only
  • obtain cumulated tips by sex and time of the day
# Example 1 -----

qrobj = qr.Querier(df)

df1 = qrobj\
.select(req="tip, sex, smoker, time")\
.filtr(req="smoker == 'No'")\
.summarize(req="sum(tip), sex, time", group_by="sex, time")\

sum_tip     sex    time
0    88.28  Female  Dinner
1    61.49  Female   Lunch
2   243.17    Male  Dinner
3    58.83    Male   Lunch

The query could be written in one line, but it would be less readable (hence the “\” for line continuation).


  • select columns tip, sex, day, size from tips dataset
  • filter rows corresponding to weekends only
  • obtain average tips by sex and day
# Example 2 -----

df2 = qr.Querier(df)\
.select(req='tip, sex, day, size')\
.filtr(req="(day == 'Sun') | (day == 'Sat')")\
.summarize(req="avg(tip), sex, day", group_by="sex, day")\

    avg_tip     sex  day
0  2.801786  Female  Sat
1  3.367222  Female  Sun
2  3.083898    Male  Sat
3  3.220345    Male  Sun

A notebook containing these results can be found here. Contributions/remarks are welcome as usual, you can submit a pull request on Github.

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