I want to take a pandas dataframe, do a count of unique elements by a column and retain 2 of the columns. But I get a multi-index dataframe after groupby which I am unable to (1) flatten (2) select only relevant columns. Here is my code:
import pandas as pd
df = pd.DataFrame({
'ID':[1,2,3,4,5,1],
'Ticker':['AA','BB','CC','DD','CC','BB'],
'Amount':[10,20,30,40,50,60],
'Date_1':['1/12/2018','1/14/2018','1/12/2018','1/14/2018','2/1/2018','1/12/2018'],
'Random_data':['ax','','nan','','by','cz'],
'Count':[23,1,4,56,34,53]
})
df2 = df.groupby(['Ticker']).agg(['nunique'])
df2.reset_index()
print(df2)
df2 still comes out with two levels of index. And has all the columns: Amount, Count, Date_1, ID, Random_data.
How do I reduce it to one level of index?
And retain only ID and Random_data columns?
Try this instead:
1) Select only the relevant columns (['ID', 'Random_data']
)
2) Don't pass a list to .agg
- just 'nunique'
- the list is what is causing the multi index behaviour.
df2 = df.groupby(['Ticker'])['ID', 'Random_data'].agg('nunique')
df2.reset_index()
Ticker ID Random_data
0 AA 1 1
1 BB 2 2
2 CC 2 2
3 DD 1 1
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