I have 3 Data-frames of Following Shapes:
(34376, 13), (52389, 28), (16531, 14)
This is the First Dataframe which we have:
This is the Second Dataframe which we have:
This the Third Dataframe which we have:
Now, as I have mentioned the shapes of all the Dataframes, the main task is we have to merge this on the Accession Number \
DF1-has the exact 34376 Accession which we want.
DF2- has around 28000 Accession which we want. This basically means that the remaining Accession of that table we don't want.
DF3- has around 9200 Accession which we want
How can we, merge all these 3 DF's on Accession Number, so that we get the extra columns of DF2,DF3 merged with DF1 on Accession Number. Also, we can see that DF2 has 52389 columns, so if there are same Accession Numbers repeated in DF2, we still want to merge it, but the rows of DF1 should be repeated while merged with the extra rows of DF2 and same with DF3. The Accession where no values are available in DF2/DF3 but present in DF1, the rows should become Null.
You can simply use the pandas merge function
pd.merge(pd.merge(df1,df2,on='ACCESSION_NUMBER'),df3,on='ACCESSION_NUMBER')
or
df1.merge(df2,on='ACCESSION_NUMBER').merge(df3,on='ACCESSION_NUMBER')
or
You could use the reduce class from functools library
reduce(lambda x,y: pd.merge(x,y, on='ACCESSION_NUMBER', how='outer'), [df1, df2, df3])
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 am new to python and I'm currently working on a project where I have to merge two dataframes. One dataframe, which is called cancer_df, is cancer incidencies by county, year, sex, gender, etc. The other dataframe, which is called hspa_df, is a health score by county and year (FYI, it's only counties in California). I would like to combine my two dataframe on county and year. Here is the cancer dataframe before the merge and Here is the hspa dataframe before the merge
Then I imported my data and tried the following merge:
merged_df= pd.merge(cancer_df, hspa_df, on="County" , how="outer")
However, this seems to append the data not merge it. It adds my hspa_df at the end and fills the top of the variable they share in common as NaNs. Why is this happening? I have successfully used this merge with other dataframes, but i merged them on numerical columns, not string.
Here is the merged dataframes head and Here is the merged dataframes tail
I would like to combine my two dataframe on county and year
merged_df = pd.merge(cancer_df, hspa_df, on=['County', 'Year'] )
whether you want to do inner, left, right, etc. join, depends on your usecase, but note how to specify two columns.
It fills the top of the variable they share in common as NaNs
This is what an outer join does, and it uses fillers for that.
I am working on a project and at one point I need to left join two dataframes: df and temp.
df has around 20 columns and 47576 rows while temp has 4 columns and 446829 rows; the two dataframe have to be joined on three columns (shared by the both of them).
To avoid creating extra Lines I first run the following:
temp = temp.drop_duplicates(subset=['A','B','C'])
Then I join the two dataframes running the function:
df_1 = pd.merge(df, temp, how='left', left_on=['A','B','C']; right_on=['A','B','C'])
I would then assume that the df_1 dataframe has exactly as many rows as df (since it can't have more as I have already dropped the duplicates in temp; and it shouldn't have less as it is a left join).
But I see that actually the df_1 dataframe has 30259 rows which is much less than the 47576 rows the df dataframe had.
How is this possible?
(Also, thinking it could somehow help I filled in the Nan values of the columns 'A','B','C' in the df dataframe but it doesn't seem to help)
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 2 data frames
DF1:
DF2:
The common column is Fin, but the DF1 Plo column should maintain its order and the data in DF2 should be inserted in between on the right side to create another DF like below, with DF1 on the left and DF2 on the right and the common column Fin in the middle
Expected Output:
i tried this
new=pd.concat([i.set_index('Fin') for i in [pdadata1,pdadata2]],axis=1, join='outer')
I am not sure on how to make the DF1 on the left and Fin in the middle, any help would be nice
This is called an "outer join". Look at the merge method with parameter how='outer'.