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')
Related
I am trying to merge multiple dataframes and create a new dataframe containing only the common the rows. For example:
The dataframes that i have as input:enter image description here
The dataframe that i want to have as output:enter image description here
Do you know if there is a way to do that? If you could help me, i would be more than thankfull!!
Thanks,
Eleni
You want df.merge:
df = df1.merge(df2, how='inner', indicator=False)
.merge(df3, how='inner', indicator=False)
I have 2 dataframes. One has a bunch of columns including f_uuid. The other dataframe has 2 columns, f_uuid and i_uuid.
the first dataframe may contain some f_uuids that the second dataframe doesn't and vice versa.
I want the first dataframe to have a new column i_uuid (from the second dataframe) populated with the appropriate values for the matching f_uuid in that first dataframe.
How would I achieve this?
df1 = pd.merge(df1,
df2,
on='f_uuid')
If you want to keep all f_uuid from df1 (e.g. those not available in df2), you may run
df1 = pd.merge(df1,
df2,
on='f_uuid',
how='left')
I think what your looking for is a merge : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html?highlight=merge#pandas.DataFrame.merge
In your case, that would look like :
bunch_of_col_df.merge(other_df, on="f_uuid")
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
Given 2 data frames like the link example, I need to add to df1 the "index income" from df2. I need to search by the df1 combined key in df2 and if there is a match return the value into a new column in df1. There is not an equal number of instances in df1 and df2 and there are about 700 rows in df1 1000 rows in df2.
I was able to do this in excel with a vlookup but I am trying to apply it to python code now.
This should solve your issue:
df1.merge(df2, how='left', on='combind_key')
This (left join) will give you all the records of df1 and matching records from df2.
https://www.geeksforgeeks.org/how-to-do-a-vlookup-in-python-using-pandas/
Here is an answer using joins. I modified my df2 to only include useful columns then used pandas left join.
Left_join = pd.merge(df,
zip_df,
on ='State County',
how ='left')
I have two multi-index dataframes which should have the same index names (barring any format issues) that I want to join together.
DF1 =
DF2 =
I want to join DF2 to DF1 only where the NAME and code match so on this dataframe for example, it would pull in qty for 10'' of 69.0
I've tried different variations of join, concat and multiindex.join but can't see to figure it out. Any help much appreciated!