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)
Related
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")
I have a dataframe df1 that is the list of e-mails of people that downloaded a certain e-book, and another dataframe df2 that is the e-mails of people that downloaded a different e-book.
I want to find the people that downloaded both e-books, or the common values between df1 and df2, using Python.
Is it possible to do that? How?
This was already discussed. Can you click on the below link
Find the common values in columns in Pandas dataframe
Assuming the two data frames as df1 and df2 with email column, you can do the following:
intersected_df = pd.merge(df1, df2, how='inner')
This data frame will have the values corresponding to emails found in df1 and df2
Dump the emails from df1 into a set, in order to avoid duplicates.
Dump the emails from df2 into a set, for the same reason.
Find the intersection of these two sets, as such:
set1 = set(df1.Emails)`
set2 = set(df2.Emails)
common = set1.intersection(set2)```
I believe you should merge the two dataframes
merged = pd.merge(df1, df1, how='inner', on=['e-mails'])
and then drop the Nan values:
merged.dropna(inplace=True)
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 have a problem.
I have made 3 queries to 3 different tables on a database where the data is similar and stores the values on 3 different dataframes.
My question is: Is there any way to make a new data frame where the column is a Dataframe?
Like this image
https://imgur.com/pATNi80
Thank you!
I do not know what exactly you need but you can try this:-
pd.DataFrame([d["col_name"] for d in df])
Where df is the dataframe as shown in image, col_name is the column name which you want as a separate dataframe.
Thank you to jezrael for the answer.
df = pd.concat([df1, df2, df3], axis=1, keys=('df1','df2','df3'))
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')