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.
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")
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 dataframes: df1 and df2.
I want to eliminate all occurrences of df2 rows in df1.
Basically, this is the set difference operator but for dataframes.
My ask is very similar to this question with one major variation that its possible that df1 may have no common rows at all. In that case, if we concat the two dataframes and then drop the duplicates, it still doesn't eliminate df2 occurrences in df1. Infact it adds to it.
The question is also similar to this, except that I want my operation on the rows.
Example:
Case 1:
df1:
A,B,C,D
E,F,G,H
df2:
E,F,G,H
Then, df1-df2:
A,B,C,D
Case 2:
df1:
A,B,C,D
df2:
E,F,G,H
Then, df1 - df2:
A,B,C,D
Spoken simply, I am looking for a way to do df1 - df2 (remove df2 if present in df1). How should this be done?
Set difference will work here, it returns unique values in ar1 that are not in ar2.
np.setdiff1d(df1, df2)
Or to get the result in form of DataFrame,
pd.DataFrame([np.setdiff1d(df1, df2)])
try:
df1[~df1.isin(df2)]
A,B,C,D
Does anyone know of an efficient way to create a new dataframe based off of two dataframes in Python/Pandas?
What I am trying to do is check if a value from df1 is in df2, then do not add the row to df3. I am working with student IDS, and if a student ID from df1 is in df2, I do not want to include it in the new dataframe, df3.
So does anybody know an efficient way to do this? I have googled and looked on SO, but found nothing that works so far.
Assuming the column is called ID.
df3 = df1[~df1["ID"].isin(df2["ID"])].copy()
If you have both dataframes of same length you can also use:
print df1.loc[df1['ID'] != df2['ID']]
assign it to a third dataframe.
In this question this problem is solved partially to check if a row in a dataframe exists in another one.
What I have is many dataframes df1, df2, df3, df4 etc.
which are subsets of a larger dataframe df.
Now, for each row in df, I want to create a new column "RATING", and I want to assign a value.
For example if row1 in df is contained in df1 add 50 points, if it is also contained in df2 add another 30 points, in df3 add 40 points, in df4 subtract 10 points, etc.
row1 then will have a new column "RATING" with the total.
Then do the same for row2, etc.
How can I accomplish this?
Apply the exact methodology of the other question you are pointing at to get one additional boolean column per dataframe. You will end up with n extra columns being Exist_in_df1, Exist_in_df2, ..., Exist_in_dfn
Now you have a simple boolean matrix to work with against which you can apply your simple rating logic