Python 如何获取熊猫索引位置和名称

我想从pandas数据框中获取名称和索引位置，并进入如下逻辑，只是想知道是否有更好的方法来处理这个问题

import pandas as pd

df = pd.read_csv('/docs/Credit_Card.csv', encoding='ISO-8859-1')
cols = df.columns
col = [df.columns.get_loc(c) for c in df.columns if c in cols]
print(pd.DataFrame(list(zip(cols, col)),columns=['index_Name', 'Index_Number']))

结果输出：

             index_Name  Index_Number
0        Card Type Code             0
1   Card Type Full Name             1
2          Issuing Bank             2
3           Card Number             3
4    Card Holder's Name             4
5              CVV/CVV2             5
6            Issue Date             6
7           Expiry Date             7
8          Billing Date             8
9              Card PIN             9
10         Credit Limit            10

使用：

类似于：

或：

---

使用

dict

{name:column\u names:number:column\u numering}

创建数据场

In [591]: pd.DataFrame({'index_Name': df.columns, 'Index_Number': range(len(df.columns))})
Out[591]:
             index_Name  Index_Number
0        Card Type Code             0
1   Card Type Full Name             1
2          Issuing Bank             2
3           Card Number             3
4    Card Holder's Name             4
5              CVV/CVV2             5
6            Issue Date             6
7           Expiry Date             7
8          Billing Date             8
9              Card PIN             9
10         Credit Limit            10

或使用

In [621]: pd.DataFrame({'index_Name': df.columns}).rename_axis('index_Number').reset_index()
Out[621]:
             index_Name  Index_Number
0        Card Type Code             0
1   Card Type Full Name             1
2          Issuing Bank             2
3           Card Number             3
4    Card Holder's Name             4
5              CVV/CVV2             5
6            Issue Date             6
7           Expiry Date             7
8          Billing Date             8
9              Card PIN             9
10         Credit Limit            10

---

@零，很好。。你能稍微解释一下逻辑吗，虽然我看到我们在这里使用的是带范围的字典。@Zero，第二种方法看起来更优雅。@jezrael又是一种不错的方法，我只看到第一种解决方案，因为我们还需要导入

numpy

。@pygo-是，为了获得更好的性能，最好在pandas

np.arange

like

range

；）

df1 = pd.Series(df.columns).reset_index()
df1.columns = ['Index_Number','index_Name']

In [591]: pd.DataFrame({'index_Name': df.columns, 'Index_Number': range(len(df.columns))})
Out[591]:
             index_Name  Index_Number
0        Card Type Code             0
1   Card Type Full Name             1
2          Issuing Bank             2
3           Card Number             3
4    Card Holder's Name             4
5              CVV/CVV2             5
6            Issue Date             6
7           Expiry Date             7
8          Billing Date             8
9              Card PIN             9
10         Credit Limit            10

In [621]: pd.DataFrame({'index_Name': df.columns}).rename_axis('index_Number').reset_index()
Out[621]:
             index_Name  Index_Number
0        Card Type Code             0
1   Card Type Full Name             1
2          Issuing Bank             2
3           Card Number             3
4    Card Holder's Name             4
5              CVV/CVV2             5
6            Issue Date             6
7           Expiry Date             7
8          Billing Date             8
9              Card PIN             9
10         Credit Limit            10