This question already has answers here:
How to filter Pandas dataframe using 'in' and 'not in' like in SQL
(11 answers)
pandas get rows which are NOT in other dataframe
(17 answers)
Pandas Merging 101
(8 answers)
Closed 4 years ago.
I've 2 dataframes, df1 and df2, with an emails column (and other non important ones.)
I want to drop rows in df2 that contain emails that are already in df1.
How can I do that?
You can do something like this:
df_1[~df_1['email_column'].isin(df_2['email_column'].tolist())
Related
This question already has answers here:
How to filter Pandas dataframe using 'in' and 'not in' like in SQL
(11 answers)
Closed 2 years ago.
i have pandas dataframe
aa={'month':[1,2,3,4,5,6,7,8,9,10,11,12]*3,'year':[2018]*12+[2019]*12+[2020]*12}
df = pd.DataFrame(aa,columns = ['month','year'])
I want to filter only month 10 and year 2020 and 2019
how can it be done.
i am trying this but gives dataframe with zero rows.
ncdf = df.loc[(df['year'] == 2020+'|'+2019)&(df['month'] == 10)]
Filter like this:
df[(df['month']==10)&(df['year'].isin([2019,2020]))]
This question already has answers here:
How can I pivot a dataframe?
(5 answers)
Closed 2 years ago.
I have dataframe: table_revenue
how can I transpose the dataframe and have grouping by 'stations_id' to see final result as:
where values of cells is the price, aggregated by exact date (column) for specific 'station_id' (row)
It seems you need pivot_table():
output = input.pivot_table(index='station_id',columns='endAt',values='price',aggfunc='sum',fill_value=0)
This question already has answers here:
Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
(9 answers)
Closed 3 years ago.
I want to merge duplicate rows by adding a new column 'count'
Final dataframe that I want
rows can be in any order
You can use:
df["count"] = 1
df = df.groupby(["user_id", "item_id", "total"])["count"].count().reset_index()
This question already has answers here:
How to drop a list of rows from Pandas dataframe?
(15 answers)
Closed 3 years ago.
I want to clear the first 9 rows of a dataframe in Pandas.
I have to drop all rows using:
df.drop([0,1,2,3,4,5,6,7,8])
Is there a more efficient way to do this?
I have tried using a range:
df.drop([0:9])
but this does not help.
The best way to do this is by indexing: df.drop(df.index[0:9]). This example will drop the first ten rows.
This question already has answers here:
Naming returned columns in Pandas aggregate function? [duplicate]
(6 answers)
Rename result columns from Pandas aggregation ("FutureWarning: using a dict with renaming is deprecated")
(6 answers)
Closed 4 years ago.
I'm doing a group by in a pandas dataframe, how can I change the name of the aggregate column after the group by?
df.groupby(['open_year','open_month','source']).size().reset_index()
it creates a dataframe with the following columns
open_year, open_month, CREATED_BY_REVISED, 0
I', trying to rename the last colum(0) but it doesn't work
x.rename({'0':'xyz'})