How can I convert np.nan into the new pd.NA format, given the pd.DataFrame comprises float?
JavaScript
x
11
11
1
import numpy as np2
import pandas as pd3
4
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])5
df.iloc[0, 1] = 1.56
df.iloc[3, 0] = 4.77
8
df = df.convert_dtypes()9
10
type(df.iloc[0, 0]) # numpy.float64 - I'am expecting pd.NA11
Making use of pd.convert_dtypes() doesn’t seem to work when df comprises float. This conversion is however working fine when df contains int.
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Answer
From v1.2 this now works with floats by default and if you want integer use convert_floating=False parameter.
JavaScript
1
10
10
1
import numpy as np2
import pandas as pd3
4
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])5
df.iloc[0, 1] = 1.56
df.iloc[3, 0] = 4.77
8
df = df.convert_dtypes()9
df.info()10
output
JavaScript
1
11
11
1
<class 'pandas.core.frame.DataFrame'>2
Int64Index: 4 entries, 0 to 33
Data columns (total 2 columns):4
# Column Non-Null Count Dtype 5
--- ------ -------------- ----- 6
0 A 1 non-null Float647
1 B 1 non-null Float648
dtypes: Float64(2)9
memory usage: 104.0 bytes10
11
Working with ints
JavaScript
1
10
10
1
import numpy as np2
import pandas as pd3
4
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])5
df.iloc[0, 1] = 16
df.iloc[3, 0] = 47
8
df = df.convert_dtypes(convert_floating=False)9
df.info()10
output
JavaScript
1
10
10
1
<class 'pandas.core.frame.DataFrame'>2
Int64Index: 4 entries, 0 to 33
Data columns (total 2 columns):4
# Column Non-Null Count Dtype5
--- ------ -------------- -----6
0 A 1 non-null Int647
1 B 1 non-null Int648
dtypes: Int64(2)9
memory usage: 104.0 bytes10