I have a big dataframe with several columns which contains strings, numbers, etc. I am trying to group by SCENARIO and then sum only the columns between 2020 and 2050. The only thing I have got so far is sum one column as displayed as follows, but I need to change this '2050' by the columns between 2020 and 2050, for instance.
df1 = df.groupby(["SCENARIO"])['2050'].sum().sum(axis=0)
You are creating a subset of the df with only that single column. I can't tell how your dataset looks like from the information provided, but try:
df.groupby(["SCENARIO"]).sum()
This should some up all the rows which are in the column.
Alternatively select the columns which you want to perform the summation on.
df.groupby(["SCENARIO"])[["column1","column2"]].sum()
Related
I have two data frames. One of the data frames appears to be as follows:
.
Products columns contain data like 1;3;5.
The other data frame looks like:
I am merging both of the frames:
Merge_Store_Transaction['products'] = Merge_Store_Transaction['products'].str.split(';')
Merge_Store_Transaction = Merge_Store_Transaction.explode('products')
Which give me result like: It duplicated all other values that I don't want. Is there a way where it divide the profit column with respective number of products and replicate the number or just fill other rows with zero.
I think that once you have this result, you can do something like the following:
Merge_Store_Transaction["profit"] = Merge_Store_Transaction.groupby(["group_id", "date"])["profit"].mean().reset_index(0, drop=True)
Same thing for the revenue_in_usd column.
So I am new to using Python Pandas dataframes.
I have a dataframe with one column representing customer ids and the other holding flavors and satisfaction scores that looks something like this.
Although each customer should have 6 rows dedicated to them, Customer 1 only has 5. How do I create a new dataframe that will only print out customers who have 6 rows?
I tried doing: df['Customer No'].value_counts() == 6 but it is not working.
Here is one way to do it
if you post data as a code (preferably) or text, i would be able to share the result
# create a temporary column 'c' by grouping on Customer No
# and assigning count to it using transform
# finally, using loc to select rows that has a count eq 6
(df.loc[df.assign(
c=df.groupby(['Customer No'])['Customer No']
.transform('count'))['c'].eq(6]
)
I have a dataframe which has 2 columns named TABLEID and STATID
There are different values in the both the columns.
when I filter the dataframe on values say '101PC' and 'ST101', it gives me 14K records and when I filter the dataframe on values say '102HT' and 'ST102', it gives me 14K records also. The issue is when I try to combine both the filters like below it gives me blank dataframe. I was expecting 28K records in my resultant dataframe. Any help is much appreciated
df[df[['TABLEID','STATID']].apply(tuple, axis = 1).isin([('101PC', 'ST101'), ('102HT','ST102')])]
I have a grouped dataframe according to two columns.
Now i want to plot the data of Date vs Confirmed in seaborn.
Is there a good way to do it.
grouped_series = cases.groupby(['Country/Region','ObservationDate'])['Confirmed','Deaths','Recovered'].sum()
print(grouped_series)
You can change aggregatetion for grouping by datetimes only:
cases.groupby(['ObservationDate'])['Confirmed'].sum().plot()
Or if need summed values per ObservationDate and Country/Region:
cases.groupby(['Country/Region','ObservationDate'])['Confirmed'].sum().unstack(0).plot()
I have a dataframe at hourly level with several columns. I want to extract the entire rows (containing all columns) of the 10 top values of a specific column for every year in my dataframe.
so far I ran the following code:
df = df.groupby([df.index.year])['totaldemand'].apply(lambda grp: grp.nlargest(10)))
The problem here is that I only get the top 10 values for each year of that specific column and I lose the other columns. How can I do this operation and having the corresponding values of the other columns that correspond to the top 10 values per year of my 'totaldemand' column?
We usually do head after sort_values
df = df.sort_values('totaldemand',ascending = False).groupby([df.index.year])['totaldemand'].head(10)
nlargest can be applied to each group, passing the column to look for
largest values.
So run:
df.groupby([df.index.year]).apply(lambda grp: grp.nlargest(3, 'totaldemand'))
Of course, in the final version replace 3 with your actual value.
Get the index of your query and use it as a mask on your original df:
idx = df.groupby([df.index.year])['totaldemand'].apply(lambda grp: grp.nlargest(10))).index.to_list()
df.iloc[idx,]
(or something to that extend, I can't test now without any test data)