Is it possible to only merge some columns? I have a DataFrame df1 with columns x, y, z, and df2 with columns x, a ,b, c, d, e, f, etc.
I want to merge the two DataFrames on x, but I only want to merge columns df2.a, df2.b - not the entire DataFrame.
The result would be a DataFrame with x, y, z, a, b.
I could merge then delete the unwanted columns, but it seems like there is a better method.
You want to use TWO brackets, so if you are doing a VLOOKUP sort of action:
df = pd.merge(df,df2[['Key_Column','Target_Column']],on='Key_Column', how='left')
This will give you everything in the original df + add that one corresponding column in df2 that you want to join.
Target_Column be a list of columns?
df2[['key','target1','target2']]
You could merge the sub-DataFrame (with just those columns):
df2[list('xab')] # df2 but only with columns x, a, and b
df1.merge(df2[list('xab')])
list('xab') takes each element (letter) of the string 'xab' and converts it to a list element so list('xab') returns ['x', 'a', 'b']. That works if each column has a single letter as a name. In your case I think you need to do df1.merge(df2['Unique_External_Users'], *other_arguments). ...Most probably you already solved it by now, just leaving this for newbies around, like meIf you want to drop column(s) from the target data frame, but the column(s) are required for the join, you can do the following:
df1 = df1.merge(df2[['a', 'b', 'key1']], how = 'left',
left_on = 'key2', right_on = 'key1').drop(columns = ['key1'])
The .drop(columns = 'key1') part will prevent 'key1' from being kept in the resulting data frame, despite it being required to join in the first place.
KeyError: "['key1'] not found in axis"
.drop('key1', 1)
SELECT df1..., df1.,, df2.a, df2.b FROM df1 LEFT JOIN df2 ON df1.key2=df2.key1 (whereby not needed to selecting either df1.key2 or df2.key1) Also good idea to replace the original df1 with the new df1 so that this will simply add new columns based on merge.You can use .loc to select the specific columns with all rows and then pull that. An example is below:
pandas.merge(dataframe1, dataframe2.iloc[:, [0:5]], how='left', on='key')
In this example, you are merging dataframe1 and dataframe2. You have chosen to do an outer left join on 'key'. However, for dataframe2 you have specified .iloc which allows you to specific the rows and columns you want in a numerical format. Using :, your selecting all rows, but [0:5] selects the first 5 columns. You could use .loc to specify by name, but if your dealing with long column names, then .iloc may be better.
.loc will make a copy, and on a large df that can be painful. It might be better to merge then immediately take a column slice in the same expression.This is to merge selected columns from two tables.
If table_1 contains t1_a,t1_b,t1_c..,id,..t1_z columns,
and table_2 contains t2_a, t2_b, t2_c..., id,..t2_z columns,
and only t1_a, id, t2_a are required in the final table, then
mergedCSV = table_1[['t1_a','id']].merge(table_2[['t2_a','id']], on = 'id',how = 'left')
# save resulting output file
mergedCSV.to_csv('output.csv',index = False)
Slight extension of the accepted answer for multi-character column names, using inner join by default:
df1 = df1.merge(df2[["Key_Column", "Target_Column1", "Target_Column2"]])
This assumes that Key_Column is the only column both dataframes have in common.
tabela_contratos=pd.read_excel("Contratos.xlsx")
tabela_emails = pd.read_excel("Emails.xlsx")
#realizar o merge
tabela_final=pd.merge(
tabela_contratos,
tabela_emails,
left_on="Cliente",
right_on="Nome",
how="inner"
)
display(tabela_final)
