TypeError:无法将序列乘以'float'类型的非整数(python 2.7) [英] TypeError: can't multiply sequence by non-int of type 'float' (python 2.7)

问题描述

我有一个数据框t_unit，它是pd.read_csv()函数的结果.

I have a dataframe t_unit, which is the result of a pd.read_csv() function.

datetime    B18_LR_T    B18_B1_T
24/03/2016 09:00    21.274  21.179
24/03/2016 10:00    19.987  19.868
24/03/2016 11:00    21.632  21.417
24/03/2016 12:00    26.285  24.779
24/03/2016 13:00    26.897  24.779

我正在重新采样数据框，以使用以下代码计算第5和第5个百分位数:

I am resampling the dataframe to calculate the 5th and 05th percentiles with the code:

keys_actual = list(t_unit.columns.values)

for key in keys_actual:
    ts_wk = t_unit[key].resample('W-MON')
    ts_wk_05p = ts_wk.apply(lambda x: x.quantile(0.05)).round(decimals=1).rename(key+'_05p', inplace=True)
    ts_wk_95p = ts_wk.apply(lambda x: x.quantile(0.95)).round(decimals=1).rename(key+'_95p', inplace=True) 

一切正常，但是当我通过pd.concat将一列添加到数据框中时:

All works fine, but when I add a column to my dataframe, by means of pd.concat, into:

datetime    B18_LR_T    B18_B1_T    ext_T
24/03/2016 09:00    21.274  21.179  6.9
24/03/2016 10:00    19.987  19.868  7.5
24/03/2016 11:00    21.632  21.417  9.1
24/03/2016 12:00    26.285  24.779  9.9
24/03/2016 13:00    26.897  24.779  9.2

ts_wk_05p = ts_wk.apply(lambda x:x.quantile(0.05)).round(decimals = 1).rename(key +'_ 05p'，inplace = True)

ts_wk_05p = ts_wk.apply(lambda x: x.quantile(0.05)).round(decimals=1).rename(key+'_05p', inplace=True)

TypeError:无法将序列乘以'float'类型的非整数

TypeError: can't multiply sequence by non-int of type 'float'

你知道为什么吗?

推荐答案

某些列不是数字存在问题. 您可以检查 dtypes :

There is problem some column is not numeric. You can check dtypes:

print (t_unit.dtypes)
B18_LR_T    float64
B18_B1_T    float64
ext_T        object
dtype: object

然后先尝试通过 astype转换为数字:

Then try convert to numeric first by astype:

t_unit.ext_T = t_unit.ext_T.astype(float)

如果:

ValueError:无法将字符串转换为浮点数

ValueError: could not convert string to float

然后将 to_numeric 与参数errors='coerce'用于将错误数据转换为NaN:

then use to_numeric with parameter errors='coerce' for convert bad data to NaN:

t_unit.ext_T = pd.to_numeric(t_unit.ext_T, errors='coerce')

所有代码:

#simulate string column
t_unit.ext_T = t_unit.ext_T.astype(str)
print (t_unit.dtypes)
B18_LR_T    float64
B18_B1_T    float64
ext_T        object
dtype: object

#convert to float
t_unit.ext_T = t_unit.ext_T.astype(float)

print (t_unit)

L = []
for key in t_unit.columns:
    ts_wk = t_unit[key].resample('W-MON')
    #remove inplace=True
    ts_wk_05p = ts_wk.apply(lambda x: x.quantile(0.05)).round(decimals=1).rename(key+'_05p')
    ts_wk_95p = ts_wk.apply(lambda x: x.quantile(0.95)).round(decimals=1).rename(key+'_95p') 
    L.append(ts_wk_05p)
    L.append(ts_wk_95p)


print (pd.concat(L, axis=1))
            B18_LR_T_05p  B18_LR_T_95p  B18_B1_T_05p  B18_B1_T_95p  ext_T_05p  \
datetime                                                                        
2016-03-28          20.2          26.8          20.1          24.8        7.0   

            ext_T_95p  
datetime               
2016-03-28        9.8  

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