pandas 混合型到整数 [英] Pandas Mixed Type to Integer

问题描述

给出以下数据框:

import pandas as pd
df = pd.DataFrame(
        {'A':['A','B','C','D'],
         'C':['1','12','*','8']
        })
df

    A   C
0   A   1
1   B   12
2   C   *
3   D   8

我想删除所有'*'实例，并将其余实例转换为整数. 我的实际数据中可能存在"nan"或"NaN"的某些情况.

I'd like to remove all instances of '*' and convert the rest to integer. There may be some instances of 'nan' or 'NaN' in my actual data.

推荐答案

您可以使用pd.to_numeric将C列转换为数字值.传递errors='coerce'告诉pd.to_numeric将非数字值设置为NaN.

You could use pd.to_numeric to convert the C column to numeric values. Passing errors='coerce' tells pd.to_numeric to set non-numeric values to NaN.

import pandas as pd

df = pd.DataFrame(
        {'A':['A','B','C','D'],
         'C':['1','12','*','8'] })

df['C'] = pd.to_numeric(df['C'], errors='coerce')
print(df)

打印

   A     C
0  A   1.0
1  B  12.0
2  C   NaN
3  D   8.0

由于仅在具有浮点dtype(或object dtype)的列中允许使用NaN值，因此无法将该列设置为整数dtype.

Since NaN values are only allowed in columns with floating-point dtype (or object dtype), the column can not be set to an integer dtype.

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