I'm new to programming and Pandas. Therefore, please do not judge strictly.
To this table, I need to add a new column of values got from other columns.
inp = [{'Date':2003, 'b1':5,'b2':0,'b3':4,'b4':3},{'Date':2003, 'b1':2,'b2':2,'b3':1,'b4':8},{'Date':2004, 'b1':2,'b2':3,'b3':1,'b4':1},{'Date':2004, 'b1':1,'b2':8,'b3':2,'b4':1},{'Date':2005, 'b1':2,'b2':1,'b3':6,'b4':2},{'Date':2006, 'b1':1,'b2':7,'b3':2,'b4':9}]
df = pd.DataFrame(inp)
print (df)
Date b1 b2 b3 b4
0 2003 5 0 4 3
1 2003 2 2 1 8
2 2004 2 3 1 1
3 2004 1 8 2 1
4 2005 2 1 6 2
5 2006 1 7 2 9
Namely, depending on the date. That is if the value of column "Date" == 2003 - I need to get the value from column b1, if the value of column "Date" = 2004 then I need to get the value from column b2, "Date" = 2004 - column b3 and so on. So the values of new column should be: 5,2,3,8,6,9.
I have a dictionary of correspondences smt. like:
Corr_dict = {2003:'b1',2004:'b2',2005:'b4',2006:'b7'...}
This is just an example. I have a large dataset, so I want to understand the mechanics.
Sorry for the poor question formatting. I will be very grateful for any help.
expected output
Date b1 b2 b3 b4 vals
0 2003 5 0 4 3 5.0
1 2003 2 2 1 8 2.0
2 2004 2 3 1 1 3.0
3 2004 1 8 2 1 8.0
4 2005 2 1 6 2 6.0
5 2006 1 7 2 9 9.0
I'd use df.lookup:
df['Correspond'] = df.lookup(df.index, df['Date'].map(dd))
MCVE:
import pandas as pd
import numpy as np
inp = [{'Date':2003, 'b1':5,'b2':0,'b3':4,'b4':3},{'Date':2003, 'b1':2,'b2':2,'b3':1,'b4':8},{'Date':2004, 'b1':2,'b2':3,'b3':1,'b4':1},{'Date':2004, 'b1':1,'b2':8,'b3':2,'b4':1},{'Date':2005, 'b1':2,'b2':1,'b3':6,'b4':2},{'Date':2006, 'b1':1,'b2':7,'b3':2,'b4':9}]
df = pd.DataFrame(inp)
dd = {2003:'b1', 2004:'b2', 2005:'b3', 2006:'b4'}
df['Correspond'] = df.lookup(df.index, df['Date'].map(dd))
print(df)
output:
Date b1 b2 b3 b4 Correspond
0 2003 5 0 4 3 5
1 2003 2 2 1 8 2
2 2004 2 3 1 1 3
3 2004 1 8 2 1 8
4 2005 2 1 6 2 6
5 2006 1 7 2 9 9