Convert a python dataframe with multiple rows into one row using python pandas?
apply, dataframe, pandas, python
Solution
If you are still looking for an answer using groupby
df = df.groupby('device_id')['p_food', 'p_phone'].apply(lambda x: pd.DataFrame(x.values)).unstack().reset_index()
df.columns = df.columns.droplevel()
df.columns = ['device_id','p_food_1', 'p_food_2', 'p_phone_1','p_phone_2']
You get
device_id p_food_1 p_food_2 p_phone_1 p_phone_2
0 0 0.2 0.1 0.8 0.9
1 1 0.3 0.5 0.7 0.5
2 2 0.1 0.7 0.9 0.3
Problem
Having the following dataframe, ``` df = pd.DataFrame({'device_id' : ['0','0','1','1','2','2'], 'p_food' : [0.2,0.1,0.3,0.5,0.1,0.7], 'p_phone' : [0.8,0.9,0.7,0.5,0.9,0.3] }) print(df) ``` output: ``` device_id p_food p_phone 0 0 0.2 0.8 1 0 0.1 0.9 2 1 0.3 0.7 3 1 0.5 0.5 4 2 0.1 0.9 5 2 0.7 0.3 ``` How to achieve this transformation? ``` df2 = pd.DataFrame({'device_id' : ['0','1','2'], 'p_food_1' : [0.2,0.3,0.1], 'p_food_2' : [0.1,0.5,0.7], 'p_phone_1' : [0.8,0.7,0.9], 'p_phone_2' : [0.9,0.5,0.3] }) print(df2) ``` Output: ``` device_id p_food_1 p_food_2 p_phone_1 p_phone_2 0 0 0.2 0.1 0.8 0.9 1 1 0.3 0.5 0.7 0.5 2 2 0.1 0.7 0.9 0.3 ``` I try to achieve it use groupby,apply,agg... But I still can't achieve this transformation. Update My final Code: ``` df.drop_duplicates('device_id', keep='first').merge(df.drop_duplicates('device_id', keep='last'),on='device_id') ``` I appreciated su79eu7k's and A-Za-z's time and effort. Words are not enough to express my gratitude.