pandas 可以读取的文件格式有很多，这里主要介绍读取 csv, excel, txt 文件。
我将以下表格内容分别存储在 csv、excel、txt 文件中，并分别读取它们。
seqpricecategorydatea1.4apple2022/7/1b3.4banana2022/7/5c2.5orange2022/7/12d1.2lemon2022/7/30
pandas读取csv文件
import pandas as pd
df_csv = pd.read_csv('my_csv.csv')
print(df_csv)
seq price category date
0 a 1.4 apple 2022-07-01
1 b 3.4 banana 2022-07-05
2 c 2.5 orange 2022-07-12
3 d 1.2 lemon 2022-07-30
pandas读取txt文件
`python
import pandas as pd
df_txt = pd.read_table(‘my_txt.txt’)
print(df_txt)
0 1 2 3
0 seq price category date
1 a 1.4 apple 2022/7/1
2 b 3.4 banana 2022/7/5
3 c 2.5 orange 2022/7/12
4 d 1.2 lemon 2022/7/30
df_csv = pd.read_csv(‘my_csv.csv’, index_col=’category’)
print(df_csv)
price category
seq date
a 2022/7/1 1.4 apple
b 2022/7/5 3.4 banana
c 2022/7/12 2.5 orange
d 2022/7/30 1.2 lemon
df_csv = pd.read_csv(‘my_csv.csv’, usecols=[‘seq’, ‘category’])
print(df_csv)
seq price category date
0 a 1.4 apple 2022-07-01
1 b 3.4 banana 2022-07-05
2 c 2.5 orange 2022-07-12
3 d 1.2 lemon 2022-07-30
df_csv = pd.read_csv(‘my_csv.csv’, nrows=2)
print(df_csv)
seq, price, category, date
0 a, 1.4, apple, 2022/7/1
1 b, 3.4, banana, 2022/7/5
2 c, 2.5, orange, 2022/7/12
3 d, 1.2, lemon, 2022/7/30
df_txt = pd.read_table(‘my_txt.txt’, sep=’,’)
print(df_txt)
seq |||| category
0 a |||| apple
1 b |||| banana
2 c |||| orange
3 d |||| lemon
df_txt = pd.read_table(‘my_txt.txt’, sep=’\|\|\|\|’, engine=’python’)
print(df_txt)
| | seq | price | category | date |
|—:|:——|——–:|:———–|:———-|
| 0 | a | 1.4 | apple | 2022/7/1 |
| 1 | b | 3.4 | banana | 2022/7/5 |
| 2 | c | 2.5 | orange | 2022/7/12 |
| 3 | d | 1.2 | lemon | 2022/7/30 |
df_csv = pd.read_csv(‘my_csv.csv’)
print(df_csv.to_latex())
Original: https://blog.csdn.net/weixin_48158964/article/details/126077060
Author: 写进メ诗的结尾。
Title: 文件的读取和写入
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