Šajā rakstā mēs to apskatīsim kā atkārtot rindas DataFrame programmā Pandas .
Kā atkārtot rindas DataFrame programmā Pandas
Python ir lieliska valoda datu analīzei, galvenokārt pateicoties fantastiskai uz datiem orientētu Python pakotņu ekosistēmai. Pandas ir viena no šīm pakotnēm un padara datu importēšanu un analīzi daudz vienkāršāku.
Apskatīsim dažādus veidus, kā atkārtot rindas programmā Pandas Datu rāmis :
1. metode: Dataframe indeksa atribūta izmantošana.
Python3
lappuses uz leju tastatūra
# 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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Izvade:
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>
2. metode: Izmantojot vieta[] funkciju no datu rāmja.
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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uzraudzīta mašīnmācība
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Izvade:
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>
3. metode: Izmantojot iloc[] funkciju no DataFrame.
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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pilsēta ASV
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Izvade:
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 >
4. metode: Izmantojot iterrows () metodi no datu rāmja.
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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Izvade:
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>
5. metode: Izmantojot itertupi () datu rāmja metode.
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'>))> |
paralēla apstrāde
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in.next java
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Izvade:
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 >
6. metode: Izmantojot pieteikties () metodi no datu rāmja.
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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Izvade:
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>