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I have the following dataframe :

     Daily_KWH_System      year     month  day  hour  minute  second
 0         4136.900384      2016      9    7     0       0       0
 1         3061.657187      2016      9    8     0       0       0
 2         4099.614033      2016      9    9     0       0       0
 3         3922.490275      2016      9   10     0       0       0
 4         3957.128982      2016      9   11     0       0       0
 5         4177.014316      2016      9   12     0       0       0
 6         3077.103445      2016      9   13     0       0       0
 7         4123.103795      2016      9   14     0       0       0
..                ...       ...      ...  ...   ...     ...     ...
551               NaN       2016      11  23     0       0       0
552               NaN       2016      11  24     0       0       0
553               NaN       2016      11  25     0       0       0
..                ...       ...      ...  ...   ...     ...     ...
579               NaN       2016      11  27     0       0       0
580               NaN       2016      11  28     0       0       0

The variables type is as follows: 

print(df.dtypes)

Daily_KWH_System    object
year                 int32
month                int32
day                  int32
hour                 int32
minute               int32
second               int32

I need to convert "Daily_KWH_System" to Float, so that I use in Linear Regression model.

I tried the below code, which worked fine.

 df['Daily_KWH_System'] = pd.to_numeric(df['Daily_KWH_System'], errors='coerce')

Then I replaced the NaN's to Blank space, to use in my model. And I used the following code

 df = df.replace(np.nan,' ', regex=True)

But, again the variable " Daily_KWH_System" is getting converted to Object as soon as i replace NaN'.

Please let me know how to go about it

Improve this question
9
  • I do not need NaN in my dataframe, because the model only accepts Int/Float. Hence I need those values to be blank, so I can predict those values Commented Feb 9, 2017 at 6:59
  • Hmm, if check this question you need remove NaN values by Daily_KWH_System = df.loc[df.Daily_KWH_System.notnull(), 'Daily_KWH_System']. But maybe need something else... Commented Feb 9, 2017 at 7:13
  • But if check this - NaN are not problem - theey are removed by dropna(). Commented Feb 9, 2017 at 7:15
  • So second posible solution is Daily_KWH_System = df.Daily_KWH_System.dropna(). Please check how it works. Commented Feb 9, 2017 at 7:17
  • Or if need remove all rows where is at least one NaN - df = df.dropna() Commented Feb 9, 2017 at 7:17
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