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How to get specific rows in df based on a ID in pandas
I have a dataframe with 2 type of items (A and B).
I want to get the items with ID=1 of type A but not of type B.
This is what I tried.
import pandas as pd
# create data
df = pd.DataFrame({‘id’ : [1,2,3,1,2,3],
‘type’:[‘A’, ‘A’, ‘A’, ‘B’, ‘B’, ‘B’]})
# Try 1
df2 = df[df.type==’A’]
print df2.id
# Try 2
df2 = df[df[‘id’] == 1]
print df2.type
Both of these do not work. Am I not able to pick 1st rows of ID, and choose only the A type.
A:
Option 1
If you want to keep ID only, you need to filter it and then join the dataframe
df1.type = ‘A’
df2.join(df1.iloc[df1.type.eq(‘A’)].groupby(‘id’)[‘type’].idxmax().reset_index())
Option 2
If you only want the columns of the dataframe, you can also do
df1.drop(‘type’, 1).join(df1.type.eq(‘A’)).drop(df1.type.eq(‘B’), axis=1)
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