将pandas数据框行迭代为纯文本

Question

I would like to read a dataframe (which contains tweets) row by row in order to analyze the text.

import csv
import pandas as pd

df = pd.read_csv('tweets2.csv')
df.head()

for row in df.iterrows():
    print (row)

This code I wrote does not do the job, since the "row" also includes the index. Instead, I want the simple text, that I will process further.

Answer 1

You could use df.values :

for row in df.values:
    print(row)

Example:

df = pd.DataFrame({'Col1': [1, 2, 3, 4, 5], 'Col2' : ['a', 'b', 'c', 'd', 'e']})

print(df)

   Col1 Col2
0     1    a
1     2    b
2     3    c
3     4    d
4     5    e

for row in df.values:
    print(row)

[1 'a']
[2 'b']
[3 'c']
[4 'd']
[5 'e']

Answer 2

iterrows yields (index, Series) pairs

So you could unpack them in the for loop:

for i, row in df.iterrows():
    print(row)

If you don't use the i , you should change it to _ .

Using iterrows , each row is a Series . As shown by @cᴏʟᴅsᴘᴇᴇᴅ, an alternative is using values :

for row in df.values:
    print(row)

With this method each row is a numpy array (so labeling is lost).