replace nan values by mean group by date.year, date.month

[wissam1974]

Hi for all

i have read a CSV file with tow series columns as follow:
Dateobs TMIN
2006-01-01 NAN
2006-01-02 12.3
2006-01-03 11.3
..
2006-02-01 15.2
2006-02-02 Nan
2006-03-03 11.3
..
2016-04-06 15.8
2016-04-07 11.6
2016-04-08 Nan
..
etc..

Error:
my question is how to replace NaN values for the daily TMIN column by mean group by (year month)

the table shown above was issued when i execute this pseudo code

    data_pd = pd.read_csv('beirut.csv')

    # overwriting data after changing format
    data_pd["Dateobs"] = pd.to_datetime(data_pd["Dateobs"])
    Dateobs_typedatetime64=  data_pd["Dateobs"]
    print(Dateobs_typedatetime64)

    TMIN_typeFloat64=  data_pd["TMIN"]
    print(TMIN_typeFloat64)

any help or suggestions to overpass this complexity will be appreciate.
thank you

[scidam]

Your code should be something like this:

data_pd.fillna(data_pd.groupby([data_pd.Dateobs.dt.year, df.Dateobs.dt.month]).transform('mean'), inplace=True)

[wissam1974]

Your code should be something like this:

data_pd.fillna(data_pd.groupby([data_pd.Dateobs.dt.year, df.Dateobs.dt.month]).transform('mean'), inplace=True)

hi Mr first thank you for your reply, i appreciate it
i tried this

result=data_pd.fillna(data_pd.groupby([data_pd.Dateobs.dt.year, data_pd.Dateobs.dt.month]).transform('mean'), inplace=True)
print (result)

and i am getting the following result:

Output:
None

Process finished with exit code 0

still the same problem its not replace null value by mean
is there anything else i should try
thank you

[scidam]

If you are using inplace=True you don't need to assign the result. Remove inplace=True in your code, or print(data_pd). result variable is None, because when inplace=true .fillna method returns None.

This is minimal working example:

import pandas as pd
df = pd.DataFrame({'date': ['2001-09-01', '2001-09-02', '2001-10-01', '2001-10-03', '2001-09-03'],
                   'value': [1, 2, 0, None, None]})
df.date = pd.to_datetime(df.date)
df.fillna(df.groupby([df.date.dt.year, df.date.dt.month]).transform('mean'), inplace=True)

[wissam1974]

If you are using inplace=True you don't need to assign the result. Remove inplace=True in your code, or print(data_pd). result variable is None, because when inplace=true .fillna method returns None.

This is minimal working example:

import pandas as pd
df = pd.DataFrame({'date': ['2001-09-01', '2001-09-02', '2001-10-01', '2001-10-03', '2001-09-03'],
                   'value': [1, 2, 0, None, None]})
df.date = pd.to_datetime(df.date)
df.fillna(df.groupby([df.date.dt.year, df.date.dt.month]).transform('mean'), inplace=True)

Thank you a lot Mr you have helped me solve my problem

[AnkitGupta]

I want to replace NAN value of Product_price column using fillna Mean based on product ID how I can implement. Is it possible I cant implement for all columns in my dataset based on Product_id

data['product_id'] = data.groupby('product_id').product_price.transform(lambda x: x.fillna(x.mean()))