Using merge function on a column for two dataframes... how can I save the leftover values from dataframe 1 ( df_csv_deduped) and store it in some rows on the bottom of the frame
df_merged = pd.merge(df_csv_deduped, df_excel_deduped_values, how='inner', on=['Incident ID'])
You can use how='outer' to merge everything and filter then first on both, append to a new DF. Afterwards filter on left and append as well
Related
I have two dataframes.
For all rows in df1, find the corresponding row in df2 (through matching key) and update the final column in df2 to 1.
How shall I proceed in pandas?
Remove column final, use left join with indicator parameter, so is possible create 1,0 column by mapping True, False by compare both:
df = df2.drop('final', axis=1).merge(df1, how='left', indicator='final')
df['final'] = df['final'].eq('both').astype(int)
I have three separate DataFrames. Each DataFrame has the same columns - ['Email', 'Rating']. There are duplicate row values in all three DataFrames for the column Email. I'm trying to find those emails that appear in all three DataFrames and then create a new DataFrame based off those rows. So far I have I had all three DataFrames saved to a list like this dfs = [df1, df2, df3], and then concatenated them together using df = pd.concat(dfs). I tried using groupby from here but to no avail. Any help would be greatly appreciated
You want to do a merge. Similar to a join in sql you can do an inner merge and treat the email like a foreign key. Here is the docs: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html
It would look something like this:
in_common = pd.merge(df1, df2, on=['Email'], how='inner')
you could try using .isin from pandas, e.g:
df[df['Email'].isin(df2['Email'])]
This would retrieve row entries where the values for the column email are the same in the two dataframes.
Another idea is maybe try an inner merge.
Goodluck, post code next time.
I am trying to merge those two dataframes in order to replace in the left one values that are present in the right one with the same ticker and datetime.
Here is a small example
Here's a way using update:
# update uses index matching
left_df = left_df.set_index('Timestamp')
right_df = right_df.set_index('Timestamp')
# update does inplace modification, so returns nothing.
left_df.update(right_df)
print(left_df)
I have two df named "df" and second as "topwud".
df
topwud
when I join these two dataframes bt inner join using BOMCPNO and PRTNO as the join column
like
second_level=pd.merge(df,top_wud ,left_on='BOMCPNO', right_on='PRTNO', how='inner').drop_duplicates()
Then I got this data frame
Result
I don't want common name coming as PRTNO_x and PRTNO_y , I want to keep only PRTNO_x in my result dataframe as name "PRTNO" which is default name.
Kindly help me :)
try This -
pd.merge(df1, top_wud, on=['BOMCPNO', 'PRTNO'])
What this will do though is return only the values where BOMCPNO and PRTNO exist in both dataframes as the default merge type is an inner merge.
So what you could do is compare this merged df size with your first one and see if they are the same and if so you could do a merge on both columns or just drop/rename the _x/_y suffix B columns.
I would spend time though determining if these values are indeed the same and exist in both dataframes, in which case you may wish to perform an outer merge:
pd.merge(df1, df2, on=['A', 'B'], how='outer')
Then what you could do is then drop duplicate rows (and possibly any NaN rows) and that should give you a clean merged dataframe.
merged_df.drop_duplicates(cols=['BOMCPNO', 'PRTNO'],inplace=True)
also try other types of join , as i dont know what exactly you want, i think its left inner .
check this if it solved your problem.
I have two pandas dataframes both holding irregular timeseries data.
I want merge/join the two frames by time.
I also want to forward fill the other columns of frame2 for any "new" rows that were added through the joining process. How can I do this?
I have tried:
df = pd.merge(df1, df2, on="DateTime")
but this just leave a frame with matching timestamp rows.
I would be grateful for any ideas!
Try this. The how='left' will have the merge keep all records of df1, and the fillna will populate missing values.
df = pd.merge(df1, df2, on='DateTime', how='left').fillna(method='ffill')