FORSKELLIGE MÅDER AT ITERERE OVER RÆKKER I PANDAS DATAFRAME

I denne artikel vil vi dække hvordan man itererer over rækker i en DataFrame i Pandas .

Sådan itereres over rækker i en DataFrame i Pandas

Python er et fantastisk sprog til at lave dataanalyse, primært på grund af det fantastiske økosystem af datacentrerede Python-pakker. Pandaer er en af disse pakker og gør import og analyse af data meget nemmere.

Lad os se de forskellige måder at iterere over rækker i Pandas Dataramme :

Metode 1: Brug af datarammens indeksattribut.

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 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])>

Produktion:

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

Metode 2: Ved brug af placere[] fungere af datarammen.

Python3

'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'> ])>

Produktion:

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

Metode 3: Ved brug af iloc[] fungere af DataFrame.

Python3

'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> ])>

Produktion:

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

Metode 4: Ved brug af iterrows() metode af datarammen.

Python3

'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'> ])>

Produktion:

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

Metode 5: Ved brug af itertuples() metoden for datarammen.

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'> ))>

Produktion:

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

Metode 6: Ved brug af ansøge() metode af datarammen.

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> ))>

Produktion:

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