CODEWITHSUNDEEP
pythonpandasdataframe
Viktor.w
Viktor.w

Reputation: 2297

Pandas: merge two dataframe of different sizes on one column

My first dataframe (df1) looks like this:

      pvalue    trend               time                
0   0.000065    0.000076    2019-03-18 04:00:04 
1   0.000087    0.000098    2019-03-18 04:00:06 
2   0.000000    0.000000    2019-03-18 04:00:22 
3   0.000000    0.000087    2019-03-18 04:02:29 
4   0.000000    0.000000    2019-03-18 04:03:04
5   0.000000    0.000023    2019-03-18 04:03:05 
6   0.000000    0.000000    2019-03-18 04:03:18 
7   0.000000    0.000067    2019-03-18 04:18:55 
8   0.000000    0.000000    2019-03-18 04:18:56 
9   0.000000    0.000000    2019-03-18 04:20:41

My second data (df2) looks like that:

                time      price
0   2019-03-18 04:00:00  0.00190633
1   2019-03-18 04:00:01  0.00190633
2   2019-03-18 04:00:02  0.00190633
3   2019-03-18 04:00:03  0.00190633
4   2019-03-18 04:00:04  0.00190633
5   2019-03-18 04:00:05  0.00190633
6   2019-03-18 04:00:06  0.00190800
7   2019-03-18 04:00:07  0.00190800
8   2019-03-18 04:00:08  0.00190800
9   2019-03-18 04:00:09  0.00190800

df2['time'] at each row, there is a change of one second. But on df1, there are some gaps between each df1['time'] of a few seconds... what I would like to have is the following:

                time      price     pvalue  trend
0   2019-03-18 04:00:00 0.00190633 0.000000 0.000000
1   2019-03-18 04:00:01 0.00190633 0.000000 0.000000
2   2019-03-18 04:00:02 0.00190633 0.000000 0.000000
3   2019-03-18 04:00:03 0.00190633 0.000000 0.000000
4   2019-03-18 04:00:04 0.00190633 0.000065 0.000076
5   2019-03-18 04:00:05 0.00190633 0.000000 0.000000
6   2019-03-18 04:00:06 0.00190800 0.000087 0.000098

So basically have all the seconds and when there is data at pvalue and trend in df1 put them in the new dataframe. What I tried is the following: df_all =df_pvalue_trade.merge(df_check,on='time',left_index=True) but I only have the rows of df1, not every seconds like in my example... any idea? thanks!

The result I have with the above tried code is the following: 

pvalue  trend   time    mkt_result  price
6   0.000000    0.000000    2019-03-18 04:00:06 reject Ha := upward OR downward trend   0.00190800
21  0.000000    0.000000    2019-03-18 04:00:21 reject Ha := upward OR downward trend   0.00190800
22  0.000000    0.000000    2019-03-18 04:00:22 reject Ha := upward OR downward trend   0.00190800
149 0.000000    0.000000    2019-03-18 04:02:29 reject Ha := upward OR downward trend   0.00190594
184 0.000000    0.000000    2019-03-18 04:03:04 reject Ha := upward OR downward trend   0.00190594
185 0.000000    0.000000    2019-03-18 04:03:05 reject Ha := upward OR downward trend   0.00190594
198 0.000000    0.000000    2019-03-18 04:03:18 reject Ha := upward OR downward trend   0.00190594

Which is not what I want...

Upvotes: 1

Views: 45

Answers (1)

jezrael
jezrael

Reputation: 862511

Use merge with DataFrame.fillna:

df = pd.merge(df2, df1, on='time', how='left').fillna(0)
print (df)
                 time     price    pvalue     trend
0 2019-03-18 04:00:00  0.001906  0.000000  0.000000
1 2019-03-18 04:00:01  0.001906  0.000000  0.000000
2 2019-03-18 04:00:02  0.001906  0.000000  0.000000
3 2019-03-18 04:00:03  0.001906  0.000000  0.000000
4 2019-03-18 04:00:04  0.001906  0.000065  0.000076
5 2019-03-18 04:00:05  0.001906  0.000000  0.000000
6 2019-03-18 04:00:06  0.001908  0.000087  0.000098
7 2019-03-18 04:00:07  0.001908  0.000000  0.000000
8 2019-03-18 04:00:08  0.001908  0.000000  0.000000
9 2019-03-18 04:00:09  0.001908  0.000000  0.000000

Also if need replace NaNs only columns from df1.columns which are different like df2.columns:

d = dict.fromkeys(df1.columns.difference(df2.columns), 0)
print (d)
{'pvalue': 0, 'trend': 0}

df = pd.merge(df2, df1, on='time', how='left').fillna(d)
print (df)
                 time     price    pvalue     trend
0 2019-03-18 04:00:00  0.001906  0.000000  0.000000
1 2019-03-18 04:00:01  0.001906  0.000000  0.000000
2 2019-03-18 04:00:02  0.001906  0.000000  0.000000
3 2019-03-18 04:00:03  0.001906  0.000000  0.000000
4 2019-03-18 04:00:04  0.001906  0.000065  0.000076
5 2019-03-18 04:00:05  0.001906  0.000000  0.000000
6 2019-03-18 04:00:06  0.001908  0.000087  0.000098
7 2019-03-18 04:00:07  0.001908  0.000000  0.000000
8 2019-03-18 04:00:08  0.001908  0.000000  0.000000
9 2019-03-18 04:00:09  0.001908  0.000000  0.000000

Upvotes: 1

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