convert column to numeric pandas
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)convert column to numeric pandas
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)convert dataframe to float
df["data"] = df["data"].astype(float)pandas dataframe convert string to float
df_raw['PricePerSeat_Outdoor'] = pd.to_numeric(df_raw['PricePerSeat_Outdoor'], errors='coerce')pandas object to float
df['DataFrame Column'] = df['DataFrame Column'].astype(float)object to int and float conversion pandas
In [39]:
df['2nd'] = df['2nd'].str.replace(',','').astype(int)
df['CTR'] = df['CTR'].str.replace('%','').astype(np.float64)
df.dtypes
Out[39]:
Date object
WD int64
Manpower float64
2nd int32
CTR float64
2ndU float64
T1 int64
T2 int64
T3 int64
T4 object
dtype: object
In [40]:
df.head()
Out[40]:
Date WD Manpower 2nd CTR 2ndU T1 T2 T3 T4
0 2013/4/6 6 NaN 2645 5.27 0.29 407 533 454 368
1 2013/4/7 7 NaN 2118 5.89 0.31 257 659 583 369
2 2013/4/13 6 NaN 2470 5.38 0.29 354 531 473 383
3 2013/4/14 7 NaN 2033 6.77 0.37 396 748 681 458
4 2013/4/20 6 NaN 2690 5.38 0.29 361 528 541 381Copyright © 2021 Codeinu
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