5.22. DataFrame Join

Important

pd.concat()
.merge()
.join()
.melt() - stack columns

Warning

DataFrame.append() and Series.append() have been deprecated and will be removed in Pandas 2.0. Use pandas.concat() instead [1]

5.22.1. SetUp

✘>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(0)
>>>
>>> pd.set_option('display.width', 250)
>>> pd.set_option('display.max_columns', 20)
>>> pd.set_option('display.max_rows', 30)
>>>
>>>
>>> df1999 = pd.DataFrame(
...     columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
...     index = pd.date_range('1999-12-29', periods=3),
...     data = np.random.randn(3, 4))
>>>
>>> df2000 = pd.DataFrame(
...     columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
...     index = pd.date_range('2000-01-01', periods=3),
...     data = np.random.randn(3, 4))
>>>
>>> df1999
             Morning      Noon   Evening  Midnight
1999-12-29  1.764052  0.400157  0.978738  2.240893
1999-12-30  1.867558 -0.977278  0.950088 -0.151357
1999-12-31 -0.103219  0.410599  0.144044  1.454274
>>>
>>> df2000
             Morning      Noon   Evening  Midnight
2000-01-01  0.761038  0.121675  0.443863  0.333674
2000-01-02  1.494079 -0.205158  0.313068 -0.854096
2000-01-03 -2.552990  0.653619  0.864436 -0.742165

5.22.2. Concatenate

Useful for merging data from two files or datasources

✘>>> pd.concat([df1999, df2000])
             Morning      Noon   Evening  Midnight
1999-12-29  1.764052  0.400157  0.978738  2.240893
1999-12-30  1.867558 -0.977278  0.950088 -0.151357
1999-12-31 -0.103219  0.410599  0.144044  1.454274
2000-01-01  0.761038  0.121675  0.443863  0.333674
2000-01-02  1.494079 -0.205158  0.313068 -0.854096
2000-01-03 -2.552990  0.653619  0.864436 -0.742165

5.22.3. Merge

Merge DataFrame or named Series objects with a database-style join.
The join is done on columns or indexes.
If joining columns on columns, the DataFrame indexes will be ignored.
Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.

✘>>> firstnames = pd.DataFrame({
...     'id': [1, 2, 3, 4],
...     'firstname': ['Mark', 'Melissa', 'Rick', 'Beth']})
>>>
>>> lastnames = pd.DataFrame({
...     'id': [1, 2, 3, 4],
...     'lastname': ['Watney', 'Lewis', 'Martinez', 'Johanssen']})
>>>
>>> firstnames
   id firstname
0   1      Mark
1   2   Melissa
2   3      Rick
3   4      Beth
>>>
>>> lastnames
   id   lastname
0   1     Watney
1   2      Lewis
2   3   Martinez
3   4  Johanssen
>>>
>>> firstnames.merge(lastnames)
   id firstname   lastname
0   1      Mark     Watney
1   2   Melissa      Lewis
2   3      Rick   Martinez
3   4      Beth  Johanssen
>>>
>>> firstnames.merge(lastnames, on='id')
   id firstname   lastname
0   1      Mark     Watney
1   2   Melissa      Lewis
2   3      Rick   Martinez
3   4      Beth  Johanssen
>>>
>>> firstnames.merge(lastnames, left_on='id', right_on='id')
   id firstname   lastname
0   1      Mark     Watney
1   2   Melissa      Lewis
2   3      Rick   Martinez
3   4      Beth  Johanssen
>>>
>>> firstnames.merge(lastnames).set_index('id')
   firstname    lastname
id
1      Mark     Watney
2   Melissa      Lewis
3      Rick   Martinez
4      Beth  Johanssen

✘>>> df1999.merge(df2000)
Empty DataFrame
Columns: [Morning, Noon, Evening, Midnight]
Index: []
>>>
>>> df1999.merge(df2000, right_index=True, left_index=True, how='left', suffixes=('_1999', '_2000'))
            Morning_1999  Noon_1999  Evening_1999  Midnight_1999  Morning_2000  Noon_2000  Evening_2000  Midnight_2000
1999-12-29      1.764052   0.400157      0.978738       2.240893           NaN        NaN           NaN            NaN
1999-12-30      1.867558  -0.977278      0.950088      -0.151357           NaN        NaN           NaN            NaN
1999-12-31     -0.103219   0.410599      0.144044       1.454274           NaN        NaN           NaN            NaN
>>>
>>> df1999.merge(df2000, how='outer')
    Morning      Noon   Evening  Midnight
0 -2.552990  0.653619  0.864436 -0.742165
1 -0.103219  0.410599  0.144044  1.454274
2  0.761038  0.121675  0.443863  0.333674
3  1.494079 -0.205158  0.313068 -0.854096
4  1.764052  0.400157  0.978738  2.240893
5  1.867558 -0.977278  0.950088 -0.151357

5.22.4. Join

Join columns of another DataFrame.
Join columns with other DataFrame either on index or on a key column.
Efficiently join multiple DataFrame objects by index at once by passing a list.
rfuffix - If two columns has the same name, add suffix to right
lfuffix - If two columns has the same name, add suffix to left

../../_images/pandas-dataframe-join.png — Figure 5.14. Pandas DataFrame Joins

✘>>> firstnames = pd.DataFrame({
...     'id': [1, 2, 3, 4],
...     'firstname': ['Mark', 'Melissa', 'Rick', 'Beth']})
>>>
>>> lastnames = pd.DataFrame({
...     'id': [1, 2, 3, 4],
...     'lastname': ['Watney', 'Lewis', 'Martinez', 'Johanssen']})
>>>
>>> firstnames
   id firstname
0   1      Mark
1   2   Melissa
2   3      Rick
3   4      Beth
>>>
>>> lastnames
   id   lastname
0   1     Watney
1   2      Lewis
2   3   Martinez
3   4  Johanssen

Join DataFrames using their indexes:

✘>>> firstnames.join(lastnames, lsuffix='_fname', rsuffix='_lname')
   id_fname firstname  id_lname   lastname
0         1      Mark         1     Watney
1         2   Melissa         2      Lewis
2         3      Rick         3   Martinez
3         4      Beth         4  Johanssen
>>>
>>> firstnames.set_index('id').join(lastnames.set_index('id'))
   firstname    lastname
id
1     Mark     Watney
2  Melissa      Lewis
3     Rick   Martinez
4     Beth  Johanssen

This method preserves the original DataFrame's index in the result:

✘>>> firstnames.join(lastnames.set_index('id'), on='id')
   id firstname   lastname
0   1      Mark     Watney
1   2   Melissa      Lewis
2   3      Rick   Martinez
3   4      Beth  Johanssen
>>>
>>> df1999.join(df2000, how='left', lsuffix='_1999', rsuffix='_2000')
            Morning_1999  Noon_1999  Evening_1999  Midnight_1999  Morning_2000  Noon_2000  Evening_2000  Midnight_2000
1999-12-29      1.764052   0.400157      0.978738       2.240893           NaN        NaN           NaN            NaN
1999-12-30      1.867558  -0.977278      0.950088      -0.151357           NaN        NaN           NaN            NaN
1999-12-31     -0.103219   0.410599      0.144044       1.454274           NaN        NaN           NaN            NaN
>>>
>>> df1999.join(df2000, how='outer', lsuffix='_1999', rsuffix='_2000')
            Morning_1999  Noon_1999  Evening_1999  Midnight_1999  Morning_2000  Noon_2000  Evening_2000  Midnight_2000
1999-12-29      1.764052   0.400157      0.978738       2.240893           NaN        NaN           NaN            NaN
1999-12-30      1.867558  -0.977278      0.950088      -0.151357           NaN        NaN           NaN            NaN
1999-12-31     -0.103219   0.410599      0.144044       1.454274           NaN        NaN           NaN            NaN
2000-01-01           NaN        NaN           NaN            NaN      0.761038   0.121675      0.443863       0.333674
2000-01-02           NaN        NaN           NaN            NaN      1.494079  -0.205158      0.313068      -0.854096
2000-01-03           NaN        NaN           NaN            NaN     -2.552990   0.653619      0.864436      -0.742165

5.22.5. References

5.22.6. Assignments

# FIXME: za trudne zadanie, przenieść je do case study
# FIXME: Write solution
# FIXME: Write tests
# FIXME: English translation

# %% About
# - Name: DataFrame Join
# - Difficulty: medium
# - Lines: 25
# - Minutes: 21

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% English

# %% Polish
# 1. Na podstawie podanych URL:
#    - https://www.worldspaceflight.com/bios/eva/eva.php
#    - https://www.worldspaceflight.com/bios/eva/eva2.php
#    - https://www.worldspaceflight.com/bios/eva/eva3.php
#    - https://www.worldspaceflight.com/bios/eva/eva4.php
# 2. Scrape'uj stronę wykorzystując `pandas.read_html()`
# 3. Połącz dane wykorzystując `pd.concat`
# 4. Przygotuj plik `CSV` z danymi dotyczącymi spacerów kosmicznych
# 5. Zapisz dane do pliku
# 6. Uruchom doctesty - wszystkie muszą się powieść

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)

# >>> assert result is not Ellipsis, \
# 'Assign result to variable: `result`'
# >>> assert type(result) is pd.DataFrame, \
# 'Variable `result` must be a `pd.DataFrame` type'

>>> result  # doctest: +NORMALIZE_WHITESPACE
Ellipsis
"""

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% Imports
import pandas as pd

# %% Types
result: pd.DataFrame

# %% Data

# %% Result
result = ...