6.23. DataFrame Mapping

• DataFrame.map() - Map values of Series according to input mapping or function

• DataFrame.apply() - Apply a function along an axis of the DataFrame

• DataFrame.pipe() - Apply chainable functions that expect Series or DataFrames

6.23.1. SetUp

>>> import pandas as pd
>>>
>>> df = pd.DataFrame([
...     {'firstname': 'Alice', 'lastname': 'Apricot', 'age': 30},
...     {'firstname': 'Bob', 'lastname': 'Blackthorn', 'age': 31},
...     {'firstname': 'Carol', 'lastname': 'Corn', 'age': 32},
... ], index=['a', 'b', 'c'])

>>> df
  firstname    lastname  age
a     Alice     Apricot   30
b       Bob  Blackthorn   31
c     Carol        Corn   32

6.23.2. Map

• Works only on Series

• Argument: dict, Series, or Callable

• Works element-wise on a Series

• Operate on one element at time

• When passed a dictionary/Series will map elements based on the keys in that dictionary/Series, missing values will be recorded as NaN in the output

• Is optimised for elementwise mappings and transformation

• Operations that involve dictionaries or Series will enable pandas to use faster code paths for better performance [5]

>>> def upper(x):
...     if isinstance(x, str):
...         return x.upper()
...     return x
>>>
>>> df.map(upper)
  firstname    lastname  age
a     ALICE     APRICOT   30
b       BOB  BLACKTHORN   31
c     CAROL        CORN   32

>>> df.map(str)
  firstname    lastname age
a     Alice     Apricot  30
b       Bob  Blackthorn  31
c     Carol        Corn  32

6.23.3. Apply

• Works on both Series and DataFrame

• Argument: Callable

• On Series: operate on one element at time

• On DataFrame: elementwise but also row / column basis

• Suited to more complex operations and aggregation

• The behaviour and return value depends on the function

• Returns a scalar for aggregating operations, Series otherwise. Similarly for DataFrame.apply

• Has fastpaths when called with certain NumPy functions such as mean, sum, etc. [5]

>>> df.apply(str)
firstname    a    Alice\nb      Bob\nc    Carol\nName: firs...
lastname     a       Apricot\nb    Blackthorn\nc          C...
age          a    30\nb    31\nc    32\nName: age, dtype: i...
dtype: str

6.23.4. Differentiation

• DataFrame.map - Element-wise, operates on one element at time

• DataFrame.apply Works on a row / column basis, operates on entire rows or columns at a time

Definition:

• map defined on Series and DataFrame

• apply defined on Series and DataFrame

Argument type:

• map takes dict, Series, Callable

• apply takes Callable only

Behavior:

• map elementwise

• apply elementwise but is suited to more complex operations and aggregation; the behaviour and return value depends on the function

Use Case:

• map is good for elementwise transformations across multiple rows/columns (e.g., df[['A', 'B', 'C']].applymap(str.strip))

• apply is for applying any function that cannot be vectorised (e.g., df['sentences'].apply(nltk.sent_tokenize))

Footnotes [5]:

• map is optimised for elementwise mappings and transformation. Operations that involve dictionaries or Series will enable pandas to use faster code paths for better performance. When passed a dictionary/Series will map elements based on the keys in that dictionary/Series; missing values will be recorded as NaN in the output [1]

• apply returns a scalar for aggregating operations, Series otherwise. Note that apply also has fastpaths when called with certain NumPy functions such as mean, sum, etc. [3]

6.23.5. References

[1]

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html

[2]

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html

[3]

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html

[4]

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.applymap.html

[5] (1,2,3)

https://stackoverflow.com/questions/19798153/difference-between-map-applymap-and-apply-methods-in-pandas

6.23.6. Assignments

# %% About
# - Name: DataFrame Mapping Split
# - Difficulty: easy
# - Lines: 5
# - Minutes: 5

# %% 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
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Parse data in `datetime` column as `datetime` object
# 3. Split column `datetime` with into two separate: date and time columns
# 4. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Sparsuj dane w kolumnie `datetime` jako obiekty `datetime`
# 3. Podziel kolumnę z `datetime` na dwie osobne: datę i czas
# 4. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result  # doctest: +NORMALIZE_WHITESPACE
#       id   period            datetime   network  item           type  duration        date      time
# 0      0  1999-11 1999-10-15 06:58:00  T-Mobile  data           data      34.5  1999-10-15  06:58:00
# 1      1  1999-11 1999-10-15 06:58:00    Orange  call         mobile      13.0  1999-10-15  06:58:00
# 2      2  1999-11 1999-10-15 14:46:00      Play  call         mobile      23.0  1999-10-15  14:46:00
# 3      3  1999-11 1999-10-15 14:48:00      Plus  call         mobile       4.0  1999-10-15  14:48:00
# 4      4  1999-11 1999-10-15 17:27:00  T-Mobile  call         mobile       4.0  1999-10-15  17:27:00
# ..   ...      ...                 ...       ...   ...            ...       ...         ...       ...
# 825  825  2000-03 2000-03-13 00:38:00      AT&T   sms  international       1.0  2000-03-13  00:38:00
# 826  826  2000-03 2000-03-13 00:39:00    Orange   sms         mobile       1.0  2000-03-13  00:39:00
# 827  827  2000-03 2000-03-13 06:58:00    Orange  data           data      34.5  2000-03-13  06:58:00
# 828  828  2000-03 2000-03-14 00:13:00      AT&T   sms  international       1.0  2000-03-14  00:13:00
# 829  829  2000-03 2000-03-14 00:16:00      AT&T   sms  international       1.0  2000-03-14  00:16:00
# <BLANKLINE>
# [830 rows x 9 columns]

# %% Hints
# - `pd.Series.dt.date`
# - `pd.Series.dt.time`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.DataFrame, \
'Variable `result` has an invalid type; expected: `pd.DataFrame`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result  # doctest: +NORMALIZE_WHITESPACE
      id   period            datetime   network  item           type  duration        date      time
0      0  1999-11 1999-10-15 06:58:00  T-Mobile  data           data      34.5  1999-10-15  06:58:00
1      1  1999-11 1999-10-15 06:58:00    Orange  call         mobile      13.0  1999-10-15  06:58:00
2      2  1999-11 1999-10-15 14:46:00      Play  call         mobile      23.0  1999-10-15  14:46:00
3      3  1999-11 1999-10-15 14:48:00      Plus  call         mobile       4.0  1999-10-15  14:48:00
4      4  1999-11 1999-10-15 17:27:00  T-Mobile  call         mobile       4.0  1999-10-15  17:27:00
..   ...      ...                 ...       ...   ...            ...       ...         ...       ...
825  825  2000-03 2000-03-13 00:38:00      AT&T   sms  international       1.0  2000-03-13  00:38:00
826  826  2000-03 2000-03-13 00:39:00    Orange   sms         mobile       1.0  2000-03-13  00:39:00
827  827  2000-03 2000-03-13 06:58:00    Orange  data           data      34.5  2000-03-13  06:58:00
828  828  2000-03 2000-03-14 00:13:00      AT&T   sms  international       1.0  2000-03-14  00:13:00
829  829  2000-03 2000-03-14 00:16:00      AT&T   sms  international       1.0  2000-03-14  00:16:00
<BLANKLINE>
[830 rows x 9 columns]
"""

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

# %% Imports
import pandas as pd

# %% Types
result: pd.DataFrame

# %% Data
DATA = 'https://python3.info/_static/phones-pl.csv'

# %% Result
result = ...

# %% About
# - Name: DataFrame Mapping Translate
# - Difficulty: easy
# - Lines: 5
# - Minutes: 5

# %% 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
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Convert Polish month names to English
# 3. Parse dates to `datetime` objects
# 4. Select columns ['firstname', 'lastname', 'birthdate']
# 5. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Przekonwertuj polskie nazwy miesięcy na angielskie
# 3. Sparsuj daty do obiektów `datetime`
# 4. Wybierz kolumny ['firstname', 'lastname', 'birthdate']
# 5. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result[['firstname', 'lastname', 'birthdate']]  # doctest: +NORMALIZE_WHITESPACE
#   firstname   lastname  birthdate
# 0      Mark     Watney 1994-10-12
# 1   Melissa      Lewis 1995-07-07
# 2      Rick   Martinez 1996-01-21
# 3      Alex      Vogel 1994-11-15
# 4      Beth  Johanssen 2006-05-09
# 5     Chris       Beck 1999-08-02

# %% Hints
# - `pd.Series.replace(regex=True)`
# - `pd.to_datetime()`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.DataFrame, \
'Variable `result` has an invalid type; expected: `pd.DataFrame`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result[['firstname', 'lastname', 'birthdate']]  # doctest: +NORMALIZE_WHITESPACE
  firstname   lastname  birthdate
0      Mark     Watney 1994-10-12
1   Melissa      Lewis 1995-07-07
2      Rick   Martinez 1996-01-21
3      Alex      Vogel 1994-11-15
4      Beth  Johanssen 2006-05-09
5     Chris       Beck 1999-08-02
"""

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

# %% Imports
import pandas as pd

# %% Types
result: pd.DataFrame

# %% Data
DATA = 'https://python3.info/_static/martian-pl.csv'

MONTHS_PLEN = {'styczeń': 'January',
               'luty': 'February',
               'marzec': 'March',
               'kwiecień': 'April',
               'maj': 'May',
               'czerwiec': 'June',
               'lipiec': 'July',
               'sierpień': 'August',
               'wrzesień': 'September',
               'październik': 'October',
               'listopad': 'November',
               'grudzień': 'December'}

# %% Result
result = ...

# %% About
# - Name: DataFrame Mapping Month
# - Difficulty: easy
# - Lines: 10
# - Minutes: 8

# %% 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
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Add column `year` and `month` by parsing `period` column
# 3. Month name must be a string month name, not a number (i.e.: 'January', 'May')
# 4. Example: if `period` column is "2015-01", then `year`: 2015, `month`: January
# 5. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Dodaj kolumnę `year` i `month` poprzez sparsowanie kolumny `period`
# 3. Nazwa miesiąca musi być ciągiem znaków, a nie liczbą (i.e. 'January', 'May')
# 4. Example: jeżeli kolumna `period` jest "2015-01", to `year`: 2015, `month`: January
# 5. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result  # doctest: +NORMALIZE_WHITESPACE
#       id   period          datetime   network  item           type  duration  year     month
# 0      0  1999-11  1999-10-15 06:58  T-Mobile  data           data      34.5  1999  November
# 1      1  1999-11  1999-10-15 06:58    Orange  call         mobile      13.0  1999  November
# 2      2  1999-11  1999-10-15 14:46      Play  call         mobile      23.0  1999  November
# 3      3  1999-11  1999-10-15 14:48      Plus  call         mobile       4.0  1999  November
# 4      4  1999-11  1999-10-15 17:27  T-Mobile  call         mobile       4.0  1999  November
# ..   ...      ...               ...       ...   ...            ...       ...   ...       ...
# 825  825  2000-03  2000-03-13 00:38      AT&T   sms  international       1.0  2000     March
# 826  826  2000-03  2000-03-13 00:39    Orange   sms         mobile       1.0  2000     March
# 827  827  2000-03  2000-03-13 06:58    Orange  data           data      34.5  2000     March
# 828  828  2000-03  2000-03-14 00:13      AT&T   sms  international       1.0  2000     March
# 829  829  2000-03  2000-03-14 00:16      AT&T   sms  international       1.0  2000     March
# <BLANKLINE>
# [830 rows x 9 columns]

# %% Hints
# - `Series.str.split(expand=True)`
# - `df[ ['A', 'B'] ] = ...`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.DataFrame, \
'Variable `result` has an invalid type; expected: `pd.DataFrame`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result  # doctest: +NORMALIZE_WHITESPACE
      id   period          datetime   network  item           type  duration  year     month
0      0  1999-11  1999-10-15 06:58  T-Mobile  data           data      34.5  1999  November
1      1  1999-11  1999-10-15 06:58    Orange  call         mobile      13.0  1999  November
2      2  1999-11  1999-10-15 14:46      Play  call         mobile      23.0  1999  November
3      3  1999-11  1999-10-15 14:48      Plus  call         mobile       4.0  1999  November
4      4  1999-11  1999-10-15 17:27  T-Mobile  call         mobile       4.0  1999  November
..   ...      ...               ...       ...   ...            ...       ...   ...       ...
825  825  2000-03  2000-03-13 00:38      AT&T   sms  international       1.0  2000     March
826  826  2000-03  2000-03-13 00:39    Orange   sms         mobile       1.0  2000     March
827  827  2000-03  2000-03-13 06:58    Orange  data           data      34.5  2000     March
828  828  2000-03  2000-03-14 00:13      AT&T   sms  international       1.0  2000     March
829  829  2000-03  2000-03-14 00:16      AT&T   sms  international       1.0  2000     March
<BLANKLINE>
[830 rows x 9 columns]
"""

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

# %% Imports
import pandas as pd

# %% Types
result: pd.DataFrame

# %% Data
DATA = 'https://python3.info/_static/phones-pl.csv'

MONTHS = {
    1: 'January',
    2: 'February',
    3: 'March',
    4: 'April',
    5: 'May',
    6: 'June',
    7: 'July',
    8: 'August',
    9: 'September',
    10: 'October',
    11: 'November',
    12: 'December',
}

# %% Result
result = ...

# %% About
# - Name: DataFrame Mapping Substitute
# - Difficulty: medium
# - Lines: 10
# - Minutes: 8

# %% 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
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Select `Polish` spreadsheet
# 3. Set header and index to data from file
# 4. Mind the encoding
# 5. Substitute Polish Diacritics to English alphabet letters
# 6. Compare `df.replace(regex=True)` with `df.apply()`
# 7. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Wybierz arkusz `Polish`
# 3. Ustaw nagłówek i index na dane zaczytane z pliku
# 4. Zwróć uwagę na encoding
# 5. Podmień polskie znaki diakrytyczne na litery z alfabetu angielskiego
# 6. Porównaj `df.replace(regex=True)` z `df.apply()`
# 7. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> result
#                                              Definicja                                             Sprzet                                     Oprogramowanie                                 Kryteria wyjsciowe
# TRL
# 1    Zaobserwowanie i opisanie podstawowych zasad d...  Zebrano wiedze naukowa dotyczaca podstawowych ...  Zebrano wiedze naukowa dotyczaca podstawowych ...  Zweryfikowane publikacja badania lezacych u po...
# 2    Sformulowanie koncepcji technologicznej lub pr...  Stworzono innowacyjne rozwiazanie, zidentyfiko...  Zidentyfikowano praktyczne zastosowanie, ale m...  Udokumentowany opis aplikacji / koncepcji, kto...
# 3    Przeprowadzanie eksperymentalnie i analityczni...  Badania analityczne pozwalaja na umieszczenie ...  Opracowanie ograniczonej funkcjonalnosci do wa...  Udokumentowane wyniki analityczne / eksperymen...
# 4    Przeprowadzenie weryfikacji komponentow techno...  Stworzono niskiej wiernosci system lub jego ko...  Kluczowe, funkcjonalne komponenty oprogramowan...  Udokumentowane wyniki testow potwierdzajace zg...
# 5    Przeprowadzenie weryfikacji komponentow techno...  Stworzono sredniej wiernosci system lub jego k...  Zaimplementowane kompleksowe elementy oprogram...  Udokumentowane wyniki testow potwierdzajace zg...
# 6    Dokonanie demonstracji technologii w srodowisk...  Stworzono wysokiej wiernosci system lub jego k...  Stworzona prototypowa implementacja oprogramow...  Udokumentowane wyniki testow potwierdzajace zg...
# 7    Dokonanie demonstracji prototypu systemu w oto...  Stworzono wysokiej wiernosci system lub jego k...  Prototypowe oprogramowanie posiada wszystkie k...  Udokumentowane wyniki testow potwierdzajace zg...
# 8    Zakonczenie badan i demonstracja ostatecznej f...  Produkt koncowy w swojej ostatecznej konfigura...  Cale oprogramowanie zostalo gruntownie sprawdz...  Udokumentowane wyniki testow weryfikujacych pr...
# 9    Weryfikacja technologii w srodowisku operacyjn...  Produkt koncowy jest z powodzeniem obslugiwany...  Cale oprogramowanie zostalo gruntownie sprawdz...            Udokumentowane wyniki operacyjne misji.

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.DataFrame, \
'Variable `result` has an invalid type; expected: `pd.DataFrame`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> result  # doctest: +NORMALIZE_WHITESPACE
                                             Definicja                                             Sprzet                                     Oprogramowanie                                 Kryteria wyjsciowe
TRL
1    Zaobserwowanie i opisanie podstawowych zasad d...  Zebrano wiedze naukowa dotyczaca podstawowych ...  Zebrano wiedze naukowa dotyczaca podstawowych ...  Zweryfikowane publikacja badania lezacych u po...
2    Sformulowanie koncepcji technologicznej lub pr...  Stworzono innowacyjne rozwiazanie, zidentyfiko...  Zidentyfikowano praktyczne zastosowanie, ale m...  Udokumentowany opis aplikacji / koncepcji, kto...
3    Przeprowadzanie eksperymentalnie i analityczni...  Badania analityczne pozwalaja na umieszczenie ...  Opracowanie ograniczonej funkcjonalnosci do wa...  Udokumentowane wyniki analityczne / eksperymen...
4    Przeprowadzenie weryfikacji komponentow techno...  Stworzono niskiej wiernosci system lub jego ko...  Kluczowe, funkcjonalne komponenty oprogramowan...  Udokumentowane wyniki testow potwierdzajace zg...
5    Przeprowadzenie weryfikacji komponentow techno...  Stworzono sredniej wiernosci system lub jego k...  Zaimplementowane kompleksowe elementy oprogram...  Udokumentowane wyniki testow potwierdzajace zg...
6    Dokonanie demonstracji technologii w srodowisk...  Stworzono wysokiej wiernosci system lub jego k...  Stworzona prototypowa implementacja oprogramow...  Udokumentowane wyniki testow potwierdzajace zg...
7    Dokonanie demonstracji prototypu systemu w oto...  Stworzono wysokiej wiernosci system lub jego k...  Prototypowe oprogramowanie posiada wszystkie k...  Udokumentowane wyniki testow potwierdzajace zg...
8    Zakonczenie badan i demonstracja ostatecznej f...  Produkt koncowy w swojej ostatecznej konfigura...  Cale oprogramowanie zostalo gruntownie sprawdz...  Udokumentowane wyniki testow weryfikujacych pr...
9    Weryfikacja technologii w srodowisku operacyjn...  Produkt koncowy jest z powodzeniem obslugiwany...  Cale oprogramowanie zostalo gruntownie sprawdz...            Udokumentowane wyniki operacyjne misji.
"""

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

# %% Imports
import pandas as pd

# %% Types
result: pd.DataFrame

# %% Data
DATA = 'https://python3.info/_static/astro-trl.xlsx'

LETTERS_PLEN = {'ą': 'a', 'ć': 'c', 'ę': 'e',
                'ł': 'l', 'ń': 'n', 'ó': 'o',
                'ś': 's', 'ż': 'z', 'ź': 'z'}

# %% Result
result = ...

# %% About
# - Name: Pandas Read JSON OpenAPI
# - Difficulty: easy
# - Lines: 5
# - Minutes: 5

# %% 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
# 1. Read data from `DATA` as `df: pd.DataFrame`
# 2. Use `requests` library
# 3. Transpose data
# 4. If cell is a `dict`, then extract value for `summary`
# 5. If cell is empty, leave `None`
# 6. Run doctests - all must succeed

# %% Polish
# 1. Wczytaj dane z `DATA` jako `df: pd.DataFrame`
# 2. Użyj biblioteki `requests`
# 3. Transponuj dane
# 4. Jeżeli komórka jest `dict`, to wyciągnij wartość dla `summary`
# 5. Jeżeli komórka jest pusta, pozostaw `None`
# 6. Uruchom doctesty - wszystkie muszą się powieść

# %% Expected
# >>> list(result.columns)
# ['put', 'post', 'get', 'delete']
#
# >>> list(result.index)  # doctest: +NORMALIZE_WHITESPACE
# ['/pet', '/pet/findByStatus', '/pet/findByTags', '/pet/{petId}', '/pet/{petId}/uploadImage',
#  '/store/inventory', '/store/order', '/store/order/{orderId}',
#  '/user', '/user/createWithList', '/user/login', '/user/logout', '/user/{username}']

# %% Hints
# - `pandas.DataFrame()`
# - `DataFrame.map()`
# - `DataFrame.transpose()`

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is pd.DataFrame, \
'Variable `result` has an invalid type; expected: `pd.DataFrame`.'

>>> pd.set_option('display.max_columns', 50)
>>> pd.set_option('display.max_rows', 200)
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.memory_usage', 'deep')
>>> pd.set_option('display.precision', 4)

>>> list(result.columns)
['put', 'post', 'get', 'delete']

>>> list(result.index)  # doctest: +NORMALIZE_WHITESPACE
['/pet', '/pet/findByStatus', '/pet/findByTags', '/pet/{petId}', '/pet/{petId}/uploadImage',
 '/store/inventory', '/store/order', '/store/order/{orderId}',
 '/user', '/user/createWithList', '/user/login', '/user/logout', '/user/{username}']
"""

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

# %% Imports
import pandas as pd
import requests

# %% Types
result: pd.DataFrame

# %% Data
DATA = 'https://python3.info/_static/openapi.json'
data = requests.get(DATA).json()['paths']

# %% Result
result = ...