鑒于包含，我如何轉(zhuǎn)換np.nan為新格式？pd.NApd.DataFramefloatimport numpy as npimport pandas as pddf = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])df.iloc[0, 1] = 1.5df.iloc[3, 0] = 4.7df = df.convert_dtypes()type(df.iloc[0, 0]) # numpy.float64 - I'am expecting pd.NA使用包含pd.convert_dtypes()時似乎不起作用。但是，當包含.dffloatdfint
2 回答
偶然的你
TA貢獻1841條經(jīng)驗 獲得超3個贊
fillna 對你有用嗎？
import numpy as np
import pandas as pd
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])
df.iloc[0, 1] = 1.5
df.iloc[3, 0] = 4.7
df = df.fillna(pd.NA)
去向
A B
0 <NA> 1.5
1 <NA> <NA>
2 <NA> <NA>
3 4.7 <NA>
看類型
type(df.iloc[0, 0])
出去：
pandas._libs.missing.NAType
慕斯709654
TA貢獻1840條經(jīng)驗 獲得超5個贊
從 v1.2 開始，這現(xiàn)在默認使用浮點數(shù)，如果你想要整數(shù)使用convert_floating=False參數(shù)。
import numpy as np
import pandas as pd
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])
df.iloc[0, 1] = 1.5
df.iloc[3, 0] = 4.7
df = df.convert_dtypes()
df.info()
輸出
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 0 to 3
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 A 1 non-null Float64
1 B 1 non-null Float64
dtypes: Float64(2)
memory usage: 104.0 bytes
使用整數(shù)
import numpy as np
import pandas as pd
df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B'])
df.iloc[0, 1] = 1
df.iloc[3, 0] = 4
df = df.convert_dtypes(convert_floating=False)
df.info()
輸出
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 0 to 3
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 A 1 non-null Int64
1 B 1 non-null Int64
dtypes: Int64(2)
memory usage: 104.0 bytes
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