
        
        
            
                
                
                    df = pd.DataFrame({"A":["a","b"],"B":["c","d"]})
df
                
            
               A  B
0  a  c
1  b  d

To iterate over the rows:


        
        
            
                
                
                    for row_name, content in df.iterrows():
   print("row_name:", row_name)
   print(content)
                
            
            row_name: 0
A    a
B    c
Name: 0, dtype: object
row_name: 1
A    b
B    d
Name: 1, dtype: object

Forced casting

The iterrows(~) method return row values as a Series. This can be a problem when your row values contain multiple data types since Series can only hold one data type. To account for this, iterrows() picks a data type that can accommodate all the row values.

For instance, consider the following DataFrame:


        
        
            
                
                
                    df = pd.DataFrame({"A":[1,2],"B":[3.0,4.0]})
df
                
            
               A  B
0  1  3.0
1  2  4.0

Here, the first column is of type int, while the second column is of type float.

We iterate over the rows:


        
        
            
                
                
                    for row_name, content in df.iterrows():
   print("row_name:", row_name)
   print(content)
                
            
            row_name: 0
A    1.0
B    3.0
Name: 0, dtype: float64
row_name: 1
A    2.0
B    4.0
Name: 1, dtype: float64

Notice how the values corresponding to column A was casted from int to float. This is because float is more general than int.

Published by Isshin Inada

Edited by 0 others

Did you find this page useful?

thumb_up

thumb_down

Comment

Citation

Ask a question or leave a feedback...

Official Pandas Documentation

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html

thumb_up

thumb_down

chat_bubble_outline

settings

Enjoy our search

Hit / to insta-search docs and recipes!