Pandy konwertują float na int
df['col'] = df['col'].astype(int)
Jolly Jaguar
df['col'] = df['col'].astype(int)>>> df['C'] = df['C'].apply(np.int64)
>>> print(df)
... A B C D
... 0 8 0 1 6.226750
... 1 1 9 9 8.522808
... 2 1 4 2 7.739108df['DataFrame Column'] = df['DataFrame Column'].astype(float)
>>> df
A B C D
0 0.475103 0.355453 0.66 0.869336
1 0.260395 0.200287 NaN 0.617024
2 0.517692 0.735613 0.18 0.657106
>>> df[list("ABCD")] = df[list("ABCD")].fillna(0.0).astype(int)
>>> df
A B C D
0 0 0 0 0
1 0 0 0 0
2 0 0 0 0In [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 0.29 361 528 541 381
df['DataFrame Column'] = df['DataFrame Column'].astype(float)