Python map function with nan values-CodePudding

So I have this dataframe where category feature has both float and nan values. I want to convert all float values to integers. For that I tried

df['category'] = df['category'].apply(lambda x:int(x) if np.isnan(x)==False else x)

Unfortunately this code doesn't do anything. Why is that? And how can I modify this code for my own purpose?

Thank you

CodePudding user response：

Since integer cannot represent None/NaN values, pandas converts the series to float64.

import pandas as pd
import numpy as np


df = pd.DataFrame({"A":[0.51,None,8.0,7,0.0,-89,np.NaN]})

df.A.apply(lambda x: int(x) if not np.isnan(x) else x).apply(type)
0    <class 'float'>
1    <class 'float'>
2    <class 'float'>
3    <class 'float'>
4    <class 'float'>
5    <class 'float'>
6    <class 'float'>
Name: A, dtype: object

df.A.apply(lambda x: int(x) if not np.isnan(x) else 'FOO').apply(type)
0    <class 'int'>
1    <class 'str'>
2    <class 'int'>
3    <class 'int'>
4    <class 'int'>
5    <class 'int'>
6    <class 'str'>

CodePudding user response：

Try this: (this code convert np.nan to zero)

df["category"] = np.nan_to_num(df['category']).astype(int)

Example:

df = pd.DataFrame({"category":[1.51,None,8.0,7.0,0.0,-89,np.NaN]})
df["category"] = np.nan_to_num(df['category']).astype(int)
print(df["category"])

Output:

0     1
1     0
2     8
3     7
4     0
5   -89
6     0
Name: category, dtype: int64