How to Switch Columns Rows in a Pandas Dataframe

How to switch columns rows in a pandas dataframe

You can use df = df.T to transpose the dataframe. This switches the dataframe round so that the rows become columns.

You could also use pd.DataFrame.transpose().

How to transpose/pivot and group columns to rows in Pandas Dataframe?

Try this:

res = (df.melt(id_vars='Date', var_name='Sales_Type',value_name='Sales')
       .sort_values('Date')
       .reset_index(drop=True))

print(res)

    Date Sales_Type  Sales
0  date1     Sales1    1.1
1  date1     Sales2    1.2
2  date1     Sales3    1.3
3  date2     Sales1    2.1
4  date2     Sales2    2.2
5  date2     Sales3    2.3
6  date3     Sales1    3.1
7  date3     Sales2    3.2
8  date3     Sales3    3.3

How to transpose dataframe columns into rows in pandas

use melt:

df.melt(id_vars=['country','year','perc'])

older versions of Pandas:

pd.melt(df, id_vars=['country','year','perc'])

Output:

  country  year perc variable  value
0      IN  2015  hjk    data1     75
1      US  2015  KTM    data1    100
2      IN  2015  hjk    data2     81
3      US  2015  KTM    data2    289
4      IN  2015  hjk    data3     96
5      US  2015  KTM    data3    632

Option #2

df.set_index(['country','year','perc']).stack().reset_index()

Output:

  country  year perc level_3    0
0      IN  2015  hjk   data1   75
1      IN  2015  hjk   data2   81
2      IN  2015  hjk   data3   96
3      US  2015  KTM   data1  100
4      US  2015  KTM   data2  289
5      US  2015  KTM   data3  632

move column in pandas dataframe

You can rearrange columns directly by specifying their order:

df = df[['a', 'y', 'b', 'x']]

In the case of larger dataframes where the column titles are dynamic, you can use a list comprehension to select every column not in your target set and then append the target set to the end.

>>> df[[c for c in df if c not in ['b', 'x']] 
       + ['b', 'x']]
   a  y  b   x
0  1 -1  2   3
1  2 -2  4   6
2  3 -3  6   9
3  4 -4  8  12

To make it more bullet proof, you can ensure that your target columns are indeed in the dataframe:

cols_at_end = ['b', 'x']
df = df[[c for c in df if c not in cols_at_end] 
        + [c for c in cols_at_end if c in df]]

Pandas: How can I move certain columns into rows?

You can form two dataframe using pd.melt first and combine it back to become one dataframe.

df1 = df.melt(id_vars=['uid', 'location'], value_vars=['unit1_price', 'unit2_price', 'unit3_price'],var_name='unit',value_name='price')

df2 = df.melt(id_vars=['uid', 'location'], value_vars=['unit1_vol', 'unit2_vol', 'unit3_vol'],var_name='unit', value_name="volume")

ddf = pd.concat([df1,df2['volume']],axis=1).sort_values(by=['uid','unit'],ignore_index=True)

ddf['unit']=ddf['unit'].str.split('_',expand=True)[0]

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