DataFrame.fillna() or Series.fillna() will do this for you.

Example:

In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

To fill the NaNs in only one column, select just that column.

In [12]: df[1] = df[1].fillna(0)

In [13]: df
Out[13]: 
          0         1
0       NaN  0.000000
1 -0.494375  0.570994
2       NaN  0.000000
3  1.876360 -0.229738
4       NaN  0.000000

Or you can use the built in column-specific functionality:

df = df.fillna({1: 0})

Assuming your DataFrame is in df:

df.Temp_Rating.fillna(df.Farheit, inplace=True)
del df['Farheit']
df.columns = 'File heat Observations'.split()

First replace any NaN values with the corresponding value of df.Farheit. Delete the 'Farheit' column. Then rename the columns. Here's the resulting DataFrame:

    File  heat  Observations
0      1  YesQ            75
1      1   NoR           115
2      1  YesA            63
3      1   NoT            41
4      1   NoY            80
5      1  YesZ            12
6      2  YesQ           111
7      2   NoR            60
8      2  YesA            19
9      2   NoT            77
10     2   NoY            21
11     2  YesZ            54
12     3  YesQ            84
13     3   NoR            67
14     3  YesA            94
15     3   NoT            39
16     3   NoY            46
17     3  YesZ            81

There is no one size fits all. So you cannot assume that one technique will work the best for all the datasets.

That being said the goal of imputing missing values is to ensure that after imputation, the distribution of the column does not change. So if you have a feature that follows a left skewed distribution, then after imputation the distribution should not change much.

Following this logic use multiple imputation techniques to see which one retains the original distribution of the feature you are imputing the values for.