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")
Related
I got two dataframess; df_users like below
and df1 like below
i need to create a third dataframe called df2 in which i will get the corresponding usernames from USER_NAME column in df_users by filtering using three columns which are 'InterfaceDesc TESVLAN CVLAN'
i tried to use merge, concat and datacompy Compare functions but all failed with diffirent errors arose. please support
If you want to merge the 2 DataFrame only when the columns "InterfaceDesc", "TESVLAN", "CVLAN" are the same, you need to merge the 2 DataFrame on multiple columns and it should work:
df2 = pd.merge(df_users, df1, on=["InterfaceDesc", "TESVLAN", "CVLAN"])
If you want df2 to have only these 4 columns:
df2 = df2[["USER_NAME", "InterfaceDesc", "TESVLAN", "CVLAN"]]
I have two dataframes:
df1:
df2:
If i map the date in df2 from df1, using below merge command, which gives me output same as df1,
df2.merge(df1, how = 'left', on='Category')
But actually i need the output as below,
where, if only one date is returned, assign to the category
if multiple dates are returned and all are unique, assign the unique date once
if multiple dates are returned and if more than one unique date is available, assign None.
Required output:
can any one help with this, since i'm struggling here.
Thanks in advance
STEPS:
Use groupby and filter the required groups from the 1st dataframe.
drop the duplicates from df1
perform merge with this updated df1.
df1 = df1.groupby('Category').filter(
lambda x: x['Date'].nuique().eq(1)).drop_duplicates()
df2.merge(df1, how='left', on='Category')
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 a scenario where I want to find non-matching rows between two dataframes. Both dataframes will have around 30 columns and an id column that uniquely identify each record/row. So, I want to check if a row in df1 is different from the one in df2. The df1 is an updated dataframe and df2 is the previous version.
I have tried an approach pd.concat([df1, df2]).drop_duplicates(keep=False) , but it just combines both dataframes. Is there a way to do it. I would really appreciate the help.
The sample data looks like this for both dfs.
id user_id type status
There will be total 39 columns which may have NULL values in them.
Thanks.
P.S. df2 will always be a subset of df1.
If your df1 and df2 has the same shape, you may easily compare with this code.
df3 = pd.DataFrame(np.where(df1==df2,True,False), columns=df1.columns)
And you will see boolean output "False" for not matching cell value.
I'm trying to append two columns of my dataframe to an existing dataframe with this:
dataframe.append(df2, ignore_index = True)
and this does not seem to be working.
This is what I'm looking for (kind of) --> a dataframe with 2 columns and 6 rows:
although this is not correct and it's using two print statements to print the two dataframes, I thought it might be helpful to have a selection of the data in mind.
I tried to use concat(), but that leads to some issues as well.
dataframe = pd.concat([dataframe, df2])
but that appears to concat the second dataframe in columns rather than rows, in addition to gicing NaN values:
any ideas on what I should do?
I assume this happened because your dataframes have different column names. Try assigning the second dataframe column names with the first dataframe column names.
df2.columns = dataframe.columns
dataframe_new = pd.concat([dataframe, df2], ignore_index=True)