CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
user3848207
user3848207

Reputation: 4907

Correct way of using drop() to delete Saturday rows from panda dataframe

I have the panda dataframe in python below.

full_name  serial  Date_YMD        prc1     prc2    volume
 bottle_a  AX80    20200922    12874.50  12927.75   61023.0 
 bottle_a  AX80    20200923    12878.50  12926.75   61023.0
 bottle_a  AX80    20200924    12872.50  12928.75   61023.0
 bottle_a  AX80    20200925    12885.50  12984.25   62295.0
 bottle_a  AX80    20200926    12880.00  13000.00   14224.0

I want to delete all the rows with Date_YMD containing Saturdays.

The final dataframe will look like this;

 full_name  serial  Date_YMD        prc1     prc2    volume
 bottle_a  AX80    20200922    12874.50  12927.75   61023.0 
 bottle_a  AX80    20200923    12878.50  12926.75   61023.0
 bottle_a  AX80    20200924    12872.50  12928.75   61023.0
 bottle_a  AX80    20200925    12885.50  12984.25   62295.0

To find out whether a row is a Saturday, here is the code;

df['Date_YMD'] = pd.to_datetime(df['Date_YMD'], format='%Y%m%d')
df['Date_YMD'].dt.dayofweek.eq(5)

To drop a row, I believe the drop() function is the right one to use. If there are better options, please advise.

The problem is how do I apply drop() after finding out the Saturday rows. I am open to other methods besides using drop() as long as it solves the problem.

I am using python 3.8

Upvotes: 2

Views: 76

Answers (1)

wwnde
wwnde

Reputation: 26676

You are on the right track. Just extract day of the week using strftime and filter out non Saturdays.

 df= df[~pd.to_datetime(df['Date_YMD'], format='%Y%m%d').dt.strftime("%A").isin(['Saturday'])]

or convert days to numbers and drop any that is not equal to 6

df=df[pd.to_datetime(df['Date_YMD'], format='%Y%m%d').dt.strftime("%w")!='6']

full_name serial  Date_YMD     prc1      prc2   volume
0  bottle_a   AX80  20200922  12874.5  12927.75  61023.0
1  bottle_a   AX80  20200923  12878.5  12926.75  61023.0
2  bottle_a   AX80  20200924  12872.5  12928.75  61023.0
3  bottle_a   AX80  20200925  12885.5  12984.25  62295.0

Upvotes: 4

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