Mixing
How to assign different row's value to new column
4
I'm trying to add a column, 'C_End', to a DataFrame in Pandas that looks something like this:
df = pd.DataFrame({'ID':[123,123,123,456,456,789],
'C_ID':[8,10,35,36,40,7],
'C_Type':['New','Renew','Renew','New','Term','New'],
'Rank':[1,2,3,1,2,1]})
The new column needs to be the next 'C_Type' for each ID based on 'Rank', resulting in a DataFrame that looks like this:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
Essentially, I want to find the row where ID = ID and Rank = Rank+1 and assign C_Type to new column C_End. I've tried creating a function and using Apply (below), but that took forever and eventually gave me an error. I'm still new to Pandas and Python in general, but I feel like there has to be an easy solution that I'm not seeing.
def get_next_c_type(row):
return df.loc[(df['id'] == row['id']) & (df['rank'] == row['rank'] + 1),'c_type']
df['c_end'] = df.apply(get_next_c_type, axis = 1)
python pandas dataframe
asked Jan 2 at 22:49
AndrewAndrew
234
add a comment |
4
I'm trying to add a column, 'C_End', to a DataFrame in Pandas that looks something like this:
df = pd.DataFrame({'ID':[123,123,123,456,456,789],
'C_ID':[8,10,35,36,40,7],
'C_Type':['New','Renew','Renew','New','Term','New'],
'Rank':[1,2,3,1,2,1]})
The new column needs to be the next 'C_Type' for each ID based on 'Rank', resulting in a DataFrame that looks like this:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
Essentially, I want to find the row where ID = ID and Rank = Rank+1 and assign C_Type to new column C_End. I've tried creating a function and using Apply (below), but that took forever and eventually gave me an error. I'm still new to Pandas and Python in general, but I feel like there has to be an easy solution that I'm not seeing.
def get_next_c_type(row):
return df.loc[(df['id'] == row['id']) & (df['rank'] == row['rank'] + 1),'c_type']
df['c_end'] = df.apply(get_next_c_type, axis = 1)
python pandas dataframe
asked Jan 2 at 22:49
AndrewAndrew
234
add a comment |
4
4
4
I'm trying to add a column, 'C_End', to a DataFrame in Pandas that looks something like this:
df = pd.DataFrame({'ID':[123,123,123,456,456,789],
'C_ID':[8,10,35,36,40,7],
'C_Type':['New','Renew','Renew','New','Term','New'],
'Rank':[1,2,3,1,2,1]})
The new column needs to be the next 'C_Type' for each ID based on 'Rank', resulting in a DataFrame that looks like this:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
Essentially, I want to find the row where ID = ID and Rank = Rank+1 and assign C_Type to new column C_End. I've tried creating a function and using Apply (below), but that took forever and eventually gave me an error. I'm still new to Pandas and Python in general, but I feel like there has to be an easy solution that I'm not seeing.
def get_next_c_type(row):
return df.loc[(df['id'] == row['id']) & (df['rank'] == row['rank'] + 1),'c_type']
df['c_end'] = df.apply(get_next_c_type, axis = 1)
python pandas dataframe
asked Jan 2 at 22:49
AndrewAndrew
234
I'm trying to add a column, 'C_End', to a DataFrame in Pandas that looks something like this:
df = pd.DataFrame({'ID':[123,123,123,456,456,789],
'C_ID':[8,10,35,36,40,7],
'C_Type':['New','Renew','Renew','New','Term','New'],
'Rank':[1,2,3,1,2,1]})
The new column needs to be the next 'C_Type' for each ID based on 'Rank', resulting in a DataFrame that looks like this:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
Essentially, I want to find the row where ID = ID and Rank = Rank+1 and assign C_Type to new column C_End. I've tried creating a function and using Apply (below), but that took forever and eventually gave me an error. I'm still new to Pandas and Python in general, but I feel like there has to be an easy solution that I'm not seeing.
def get_next_c_type(row):
return df.loc[(df['id'] == row['id']) & (df['rank'] == row['rank'] + 1),'c_type']
df['c_end'] = df.apply(get_next_c_type, axis = 1)
python pandas dataframe
python pandas dataframe
asked Jan 2 at 22:49
AndrewAndrew
234
asked Jan 2 at 22:49
AndrewAndrew
234
asked Jan 2 at 22:49
AndrewAndrew
234
asked Jan 2 at 22:49
AndrewAndrew
234
asked Jan 2 at 22:49
AndrewAndrew
234
234
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
3
Try:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].transform('shift',-1)
Or as @W-B suggest:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)
Output:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 NaN
3 456 36 New 1 Term
4 456 40 Term 2 NaN
5 789 7 New 1 NaN
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
Why not justdf.groupby('ID').C_Type.shift(-1)
– Wen-Ben
Jan 2 at 23:16
I think this does not satisfyID = ID and Rank = Rank+1
– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)will work also. :) Thanks.
– Scott Boston
Jan 3 at 14:10
add a comment |
1
Here's one way using np.where:
dfs = df.shift(-1)
m1 = df.ID == dfs.ID
m2 = df.Rank + 1 == dfs.Rank
df.loc[:, 'C_End'] = np.where(m1 & m2, dfs.C_Type, None)
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
answered Jan 2 at 23:14
yatuyatu
15.6k41542
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
3
Try:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].transform('shift',-1)
Or as @W-B suggest:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)
Output:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 NaN
3 456 36 New 1 Term
4 456 40 Term 2 NaN
5 789 7 New 1 NaN
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
Why not justdf.groupby('ID').C_Type.shift(-1)
– Wen-Ben
Jan 2 at 23:16
I think this does not satisfyID = ID and Rank = Rank+1
– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)will work also. :) Thanks.
– Scott Boston
Jan 3 at 14:10
add a comment |
3
Try:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].transform('shift',-1)
Or as @W-B suggest:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)
Output:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 NaN
3 456 36 New 1 Term
4 456 40 Term 2 NaN
5 789 7 New 1 NaN
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
Why not justdf.groupby('ID').C_Type.shift(-1)
– Wen-Ben
Jan 2 at 23:16
I think this does not satisfyID = ID and Rank = Rank+1
– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)will work also. :) Thanks.
– Scott Boston
Jan 3 at 14:10
add a comment |
3
3
3
Try:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].transform('shift',-1)
Or as @W-B suggest:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)
Output:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 NaN
3 456 36 New 1 Term
4 456 40 Term 2 NaN
5 789 7 New 1 NaN
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
Try:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].transform('shift',-1)
Or as @W-B suggest:
df['C_End'] = df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)
Output:
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 NaN
3 456 36 New 1 Term
4 456 40 Term 2 NaN
5 789 7 New 1 NaN
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
edited Jan 3 at 14:11
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
answered Jan 2 at 22:59
Scott BostonScott Boston
57.7k73258
57.7k73258
Why not justdf.groupby('ID').C_Type.shift(-1)
– Wen-Ben
Jan 2 at 23:16
I think this does not satisfyID = ID and Rank = Rank+1
– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)will work also. :) Thanks.
– Scott Boston
Jan 3 at 14:10
add a comment |
Why not justdf.groupby('ID').C_Type.shift(-1)
– Wen-Ben
Jan 2 at 23:16
I think this does not satisfyID = ID and Rank = Rank+1
– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1)will work also. :) Thanks.
– Scott Boston
Jan 3 at 14:10
Why not just
df.groupby('ID').C_Type.shift(-1) – Wen-Ben
Jan 2 at 23:16
Why not just
df.groupby('ID').C_Type.shift(-1) – Wen-Ben
Jan 2 at 23:16
I think this does not satisfy
ID = ID and Rank = Rank+1– yatu
Jan 2 at 23:21
I think this does not satisfy
ID = ID and Rank = Rank+1– yatu
Jan 2 at 23:21
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,
df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1) will work also. :) Thanks.– Scott Boston
Jan 3 at 14:10
@W-B I do think we should include the sort_values by rank just to ensure the condition of getting the C_Type for the next rank. However,
df.sort_values('Rank').groupby('ID')['C_Type'].shift(-1) will work also. :) Thanks.– Scott Boston
Jan 3 at 14:10
add a comment |
1
Here's one way using np.where:
dfs = df.shift(-1)
m1 = df.ID == dfs.ID
m2 = df.Rank + 1 == dfs.Rank
df.loc[:, 'C_End'] = np.where(m1 & m2, dfs.C_Type, None)
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
answered Jan 2 at 23:14
yatuyatu
15.6k41542
add a comment |
1
Here's one way using np.where:
dfs = df.shift(-1)
m1 = df.ID == dfs.ID
m2 = df.Rank + 1 == dfs.Rank
df.loc[:, 'C_End'] = np.where(m1 & m2, dfs.C_Type, None)
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
answered Jan 2 at 23:14
yatuyatu
15.6k41542
add a comment |
1
1
1
Here's one way using np.where:
dfs = df.shift(-1)
m1 = df.ID == dfs.ID
m2 = df.Rank + 1 == dfs.Rank
df.loc[:, 'C_End'] = np.where(m1 & m2, dfs.C_Type, None)
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
answered Jan 2 at 23:14
yatuyatu
15.6k41542
Here's one way using np.where:
dfs = df.shift(-1)
m1 = df.ID == dfs.ID
m2 = df.Rank + 1 == dfs.Rank
df.loc[:, 'C_End'] = np.where(m1 & m2, dfs.C_Type, None)
ID C_ID C_Type Rank C_End
0 123 8 New 1 Renew
1 123 10 Renew 2 Renew
2 123 35 Renew 3 None
3 456 36 New 1 Term
4 456 40 Term 2 None
5 789 7 New 1 None
answered Jan 2 at 23:14
yatuyatu
15.6k41542
answered Jan 2 at 23:14
yatuyatu
15.6k41542
answered Jan 2 at 23:14
yatuyatu
15.6k41542
answered Jan 2 at 23:14
yatuyatu
15.6k41542
15.6k41542
add a comment |
add a comment |
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