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I have a Dataframe that looks something like this:
Deal Year Quarter_1 Quarter_2 Quarter_3 Financial_Data
h 1 1991 1 2 3 120
i 2 1992 4 5 6 80
j 3 1993 7 8 9 100
I want to combine all the quarters into one new column and copy the deal number, year and financial data. The end result should then look like this:
Deal Year Quarter Financial_Data
h 1 1991 1 120
i 1 1991 2 120
j 1 1991 3 120
k 2 1992 4 80
l 2 1992 5 80
m 2 1992 6 80
n 3 1993 7 100
o 3 1993 8 100
p 3 1993 9 100
937
Use the T attribute or the transpose() method to swap (= transpose) the rows and columns of pandas. DataFrame . Neither method changes the original object but returns a new object with the rows and columns swapped (= transposed object).
Split column by delimiter into multiple columnsApply the pandas series str. split() function on the “Address” column and pass the delimiter (comma in this case) on which you want to split the column. Also, make sure to pass True to the expand parameter.
Method #2: Using pivot() method. In order to convert a column to row name/index in dataframe, Pandas has a built-in function Pivot. Now, let's say we want Result to be the rows/index, and columns be name in our dataframe, to achieve this pandas has provided a method called Pivot.
You can use melt method.
df = pd.melt(d, id_vars=["Deal", "Year", "Financial_Data"],
value_name="Quarter").drop(['variable'],axis=1).sort_values('Quarter')
Output
Deal Year Financial_Data Quarter
0 1 1991 120 1
3 1 1991 120 2
6 1 1991 120 3
1 2 1992 80 4
4 2 1992 80 5
7 2 1992 80 6
2 3 1993 100 7
5 3 1993 100 8
8 3 1993 100 9
If you have many columns, you can use df.columns.tolist() method in order to achieve your requirement.
column_list = df.columns.tolist()
id_vars_list = column_list[:2] + column_list[-1:]
The statement will become
df = pd.melt(d, id_vars=id_vars_list,
value_name="Quarter").drop(['variable'],axis=1).sort_values('Quarter')
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