我在包含df= df.replace('No data', np.nan)的csv文件上使用‘No data’，而不是没有数字数据的空/空条目。使用head()方法，我看到replace方法确实用‘No data’替换了NaN条目。当我使用df.info()时，它表示每个系列的数据类型都是一个对象。
当我在Excel中打开csv文件并使用find和replace手动编辑数据时，将‘No data’更改为空白/空条目，尽管当我使用df.head()时数据框看起来完全相同，我使用df.info()它表示数据类型是浮点数。
我想知道为什么会这样，我怎样才能使我的系列的数据类型为浮点数，而不必手动编辑csv文件。
答案 0 :(得分：2)
如果列中的其他数据是数字，那么您应该使用pd.to_numeric(df, errors='coerce')
答案 1 :(得分：0)
import pandas as pd
import numpy as np
# Create data for columns with strings in it
column_data = [1,2,3] + ['no data']
# Costruct data frame with two columns
df = pd.DataFrame({'col1':column_data, 'col2':column_data[::-1]})
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
col1 4 non-null object
col2 4 non-null object
dtypes: object(2)
memory usage: 144.0+ bytes
# Replace 'no data' with Nan
df_nan = df.replace('no data', np.nan)
# Set type of all columns to float
df_result = df_nan.as_type({c:float for c in df_nan.columns})
df_result.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 2 columns):
col1 3 non-null float64
col2 3 non-null float64
dtypes: float64(2)
memory usage: 144.0 bytes
答案 2 :(得分：0)
考虑使用pandas.read_csv()中的转换器 arg，您可以在其中传递引用转换函数的列号字典。下面检查无数据字符串并有条件地用np.nan替换值，否则保持原样：
import numpy as np
import pandas as pd
c_fct = lambda x : float(x if 'No data' not in x else np.nan)
convertdict = {1:c_fct, 2:c_fct, 3:c_fct, 4:c_fct, 5:c_fct}
df = pd.read_csv('Input.csv', converters=convertdict)
输入CSV
ID Col1 Col2 Col3 Col4 Col5
TGG 0.634516647 0.900464347 0.998505978 0.170422713 0.893340128
GRI No data 0.045915333 0.718398939 0.924813864 No data
NLB 0.921127268 0.614460813 0.677857676 0.343612947 0.559437744
SEI 0.081852313 No data 0.890816385 0.943313021 0.874857844
LOY 0.632556715 0.362855866 0.038702448 0.253762859 No data
OPL 0.375088582 0.268283238 0.761552111 0.589547625 0.192223208
CTK 0.349464541 0.844718987 No data 0.841439909 0.898093646
EUE 0.629784261 0.982589843 0.315670377 0.832419474 0.950044814
JLP 0.543942659 0.988380305 0.417191823 0.823857176 0.542514099
RHK 0.728053447 0.521816539 0.402523435 No data 0.558226706
AEM 0.005495116 0.715363776 0.075508356 0.959119268 0.844730368
VLQ 0.21146319 0.558208766 0.501769554 0.226539046 0.795861461
MDB 0.230514689 0.223163664 No data 0.324636384 0.700716246
LPH 0.853433224 0.582678173 0.633109347 0.432191426 No data
PEP 0.41096305 No data .627776178 0.482359278 0.179863537
UQK 0.252598809 0.497517585 0.276060768 No data 0.087985623
KGJ 0.033985585 0.033702088 anNo data 0.286682709 0.543349787
JUQ 0.25971543 0.142067155 0.597985191 0.219841249 0.699822866
NYW No data 0.17187907 0.157413049 0.209011772 0.592824483
<强>输出
print(df)
# ID Col1 Col2 Col3 Col4 Col5
# 0 TGG 0.634517 0.900464 0.998506 0.170423 0.893340
# 1 GRI NaN 0.045915 0.718399 0.924814 NaN
# 2 NLB 0.921127 0.614461 0.677858 0.343613 0.559438
# 3 SEI 0.081852 NaN 0.890816 0.943313 0.874858
# 4 LOY 0.632557 0.362856 0.038702 0.253763 NaN
# 5 OPL 0.375089 0.268283 0.761552 0.589548 0.192223
# 6 CTK 0.349465 0.844719 NaN 0.841440 0.898094
# 7 EUE 0.629784 0.982590 0.315670 0.832419 0.950045
# 8 JLP 0.543943 0.988380 0.417192 0.823857 0.542514
# 9 RHK 0.728053 0.521817 0.402523 NaN 0.558227
# 10 AEM 0.005495 0.715364 0.075508 0.959119 0.844730
# 11 VLQ 0.211463 0.558209 0.501770 0.226539 0.795861
# 12 MDB 0.230515 0.223164 NaN 0.324636 0.700716
# 13 LPH 0.853433 0.582678 0.633109 0.432191 NaN
# 14 PEP 0.410963 NaN 0.627776 0.482359 0.179864
# 15 UQK 0.252599 0.497518 0.276061 NaN 0.087986
# 16 KGJ 0.033986 0.033702 NaN 0.286683 0.543350
# 17 JUQ 0.259715 0.142067 0.597985 0.219841 0.699823
# 18 NYW NaN 0.171879 0.157413 0.209012 0.592824
print(df.types)
# ID object
# Col1 float64
# Col2 float64
# Col3 float64
# Col4 float64
# Col5 float64
# dtype: object