W tym artykule omówimy jak iterować po wierszach w ramce danych w Pandach .
Jak iterować po wierszach w ramce danych w Pandach
Python to świetny język do analizy danych, przede wszystkim ze względu na fantastyczny ekosystem pakietów Pythona zorientowanych na dane. Pandy jest jednym z takich pakietów i znacznie ułatwia importowanie i analizowanie danych.
Zobaczmy różne sposoby iteracji po wierszach w Pandach Ramka danych :
Metoda 1: Użycie atrybutu indeksu ramki danych.
Python3
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# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,> >'Aishwarya'>,>'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,> >'Arts'>,>'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,> >'Stream'>,>'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using index attribute :
'>)> # iterate through each row and select> # 'Name' and 'Stream' column respectively.> for> ind>in> df.index:> >print>(df[>'Name'>][ind], df[>'Stream'>][ind])> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using index attribute : Ankit Math Amit Commerce Aishwarya Arts Priyanka Biology>
Metoda 2: Za pomocą miejsce[] funkcjonować ramki danych.
Python3
# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,> >'Aishwarya'>,>'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,> >'Arts'>,>'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,> >'Stream'>,> >'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using loc function :
'>)> # iterate through each row and select> # 'Name' and 'Age' column respectively.> for> i>in> range>(>len>(df)):> >print>(df.loc[i,>'Name'>], df.loc[i,>'Age'>])> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using loc function : Ankit 21 Amit 19 Aishwarya 20 Priyanka 18>
Metoda 3: Za pomocą I loc[] funkcjonować ramki danych.
Python3
# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,> >'Aishwarya'>,>'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,> >'Arts'>,>'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,> >'Stream'>,>'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using iloc function :
'>)> # iterate through each row and select> # 0th and 2nd index column respectively.> for> i>in> range>(>len>(df)):> >print>(df.iloc[i,>0>], df.iloc[i,>2>])> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using iloc function : Ankit Math Amit Commerce Aishwarya Arts Priyanka Biology >
Metoda 4: Za pomocą iterrows() metoda ramki danych.
Python3
# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,> >'Aishwarya'>,>'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,> >'Arts'>,>'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,> >'Stream'>,>'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using iterrows() method :
'>)> # iterate through each row and select> # 'Name' and 'Age' column respectively.> for> index, row>in> df.iterrows():> >print>(row[>'Name'>], row[>'Age'>])> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using iterrows() method : Ankit 21 Amit 19 Aishwarya 20 Priyanka 18>
Metoda 5: Za pomocą itertuple() metoda ramki danych.
Python3
# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,>'Aishwarya'>,> >'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,>'Arts'>,> >'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,> >'Stream'>,> >'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using itertuples() method :
'>)> # iterate through each row and select> # 'Name' and 'Percentage' column respectively.> for> row>in> df.itertuples(index>=>True>, name>=>'Pandas'>):> >print>(>getattr>(row,>'Name'>),>getattr>(row,>'Percentage'>))> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using itertuples() method : Ankit 88 Amit 92 Aishwarya 95 Priyanka 70 >
Metoda 6: Za pomocą stosować() metoda ramki danych.
Python3
# import pandas package as pd> import> pandas as pd> # Define a dictionary containing students data> data>=> {>'Name'>: [>'Ankit'>,>'Amit'>,>'Aishwarya'>,> >'Priyanka'>],> >'Age'>: [>21>,>19>,>20>,>18>],> >'Stream'>: [>'Math'>,>'Commerce'>,>'Arts'>,> >'Biology'>],> >'Percentage'>: [>88>,>92>,>95>,>70>]}> # Convert the dictionary into DataFrame> df>=> pd.DataFrame(data, columns>=>[>'Name'>,>'Age'>,>'Stream'>,> >'Percentage'>])> print>(>'Given Dataframe :
'>, df)> print>(>'
Iterating over rows using apply function :
'>)> # iterate through each row and concatenate> # 'Name' and 'Percentage' column respectively.> print>(df.>apply>(>lambda> row: row[>'Name'>]>+> ' '> +> >str>(row[>'Percentage'>]), axis>=>1>))> |
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Wyjście:
Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 2 Aishwarya 20 Arts 95 3 Priyanka 18 Biology 70 Iterating over rows using apply function : 0 Ankit 88 1 Amit 92 2 Aishwarya 95 3 Priyanka 70 dtype: object>