在Python中遍历数据框的更优雅的方法

Question

For one iterable, we can loop through using

for item in items:

But what if I have two iterables side by side, think about a pandas dataframe with 2 columns for example. I can use the above approach to loop through one column, but is there a more elegant way to loop through both columns at the same time?

import pandas as pd
df = pd.DataFrame({'col 1': [1,2,3,4,5], 'col 2': [6,7,8,9,10]})
i = 0
for j in df['col 1']:
    print(j)
    print(df['col 2'][i])
    i += 1

Thanks!

Answer 1

the zip built-in function creates iterables that aggregates whatever you pass as parameters, so this should be an alternative:

import pandas as pd
df = pd.DataFrame({'col 1': [1,2,3,4,5], 'col 2': [6,7,8,9,10]})
for i,j in zip(df['col 1'], df['col 2']):
    print(i)
    print(j)

Output:

1
6
2
7
3
8
4
9
5
10

Answer 2

You can iterate through entire rows which is more elegant:

for index, row in df.iterrows():
    print(row['col 1'], row['col 2'])

Answer 3

Use the DataFrame.itertuples() method to loop through both columns at the same time:

for i, j in df[['col 1', 'col 2']].itertuples(index=False):
    print(i)
    print(j)

Answer 4

You've already gotten some great answers to your question. However, I would also like to provide you with a different approach altogether which could be even more elegant (depending on what your end goal is).

As a general rule of thumb, you want to avoid looping through the rows of a dataframe. That tends to be slow and there's usually a better way. Try to shift your thinking into applying a function to entire "vector" (fancy word for dataframe column).

Check this out:

import pandas as pd
import numpy as np

df = pd.DataFrame({'col 1': [1,2,3,4,5], 'col 2': [6,7,8,9,10]})

def sum_2_cols(col1,col2):
    return col1 + col2

df['new_col'] = np.vectorize(sum_2_cols)(df['col 1'], df['col 2'])

The np.vectorize method is very powerful, flexible, and fast. It allows you to apply your own functions to a dataframe and it tends to perform very well. Try it out, you might get inspired to go about solving your problem in a different way.