CODEWITHSUNDEEP
pythonpandasdataframedata-science
vishal
vishal

Reputation: 269

How To select values of one dataframe based on another dataframe

I have 2 dataframe i.e df1 and df2 as follows 

df1=pd.read_csv("abc.csv")
print (df1.head(10))
df2=pd.read_csv("xyz.csv")
print (df2.head(10))

                     A              B
          0  2019-01-01 03:56:29  197.199997
          1  2019-01-01 04:02:29  197.186142
          2  2019-01-02 06:24:29  196.857986
          3  2019-01-02 06:42:29  196.816376
          4  2019-01-03 11:52:29  196.100006
          5  2019-01-03 12:00:30  196.015961
          6  2019-01-04 14:18:30  194.566376
          7  2019-01-04 14:38:30  194.356293
          8  2019-01-04 19:48:30  191.100006
          9  2019-01-05 19:56:30  191.081512

                 C                  D
          0  2019-01-1  18:00:00  1333
          1  2019-01-2  19:00:00  1.18
          2  2019-01-3  20:00:00  1666667
          3  2019-01-4  21:00:00  0
          4  2019-01-5  22:00:00  1
          5  2019-01-6  23:00:00  1.5
          6  2019-01-7  00:00:00  109
          7  2019-01-8  01:00:00  200
          8  2019-01-9  02:00:00  192
          9  2019-01-10 03:00:00  1.700000

df2 has hourly wise average data ,Now how to select values for only date in df1 where df2 column "D" has value more than 2 i.e output will look like ,

                     A           B
      0  2019-01-01 03:56:29  197.199997
      1  2019-01-01 04:02:29  197.186142
      2  2019-01-03 11:52:29  196.100006
      4  2019-01-03 12:00:30  196.015961

i have tried like 

,`final_data=pd.concat([df1.reset_index(drop=True),df2.reset_index(drop=True)],axis=1)
  final_data=final_data[final_data["D"] > 2]

but i didnt get the proper output , can any one please help me with the solution

Upvotes: 0

Views: 259

Answers (3)

vbhargav875
vbhargav875

Reputation: 887

You could try this :

import pandas as pd

df1 = pd.read_csv("file.csv")
df2 = pd.read_csv("file2.csv")

df2['C'] = pd.to_datetime(df2['C'], format='%Y-%m-%d')
dates = []

for ind in df2.index:
    if(df2['D'][ind]>2):
         date_tup = (df2['C'][ind].year,df2['C'][ind].month,df2['C'][ind].day)
         dates.append(date_tup)

df1['A'] = pd.to_datetime(df1['A'], format='%Y-%m-%d', errors='ignore')

for ind in df1.index:
    date_tup = (df1['A'][ind].year,df1['A'][ind].month,df1['A'][ind].day)
    if(date_tup not in dates):
         df1 = df1.drop([ind])

print(df1)

file1.csv :

A,B
2019-01-01 03:56:29,197.199997
2019-01-01 04:02:29,197.186142
2019-01-02 06:24:29,196.857986
2019-01-02 06:42:29,196.816376
2019-01-03 11:52:29,196.100006
2019-01-03 12:00:30,196.015961
2019-01-04 14:18:30,194.566376
2019-01-04 14:38:30,194.356293
2019-01-04 19:48:30,191.100006
2019-01-05 19:56:30,191.081512

file2.csv :

C,D
2019-01-01 18:00:00,1333
2019-01-02 19:00:00,1.18
2019-01-03 20:00:00,1666667
2019-01-04 21:00:00,0
2019-01-05 22:00:00,1
2019-01-06 23:00:00,1.5
2019-01-07 00:00:00,109
2019-01-08 01:00:00,200
2019-01-09 02:00:00,192
2019-01-10 03:00:00,1.700000

Upvotes: 1

CSQL
CSQL

Reputation: 106

Assuming that the indices of the two matrices match and that you only want to keep the information from df1 then:

df1[df2['D'] > 2]

should do the trick.

Upvotes: 0

Adarsh C
Adarsh C

Reputation: 118

Try this out:

final_data=pd.concat([df1.reset_index(drop=True),df2.reset_index(drop=True)],axis=1)
final_data=final_data[final_data["D"] > 2,['A','B']]

Upvotes: 0

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