How to Transpose Dataframe in Pandas Without Index

How do I transpose dataframe in pandas without index?

You can set the index to your first column (or in general, the column you want to use as as index) in your dataframe first, then transpose the dataframe. For example if the column you want to use as index is 'Attribute', you can do:

df.set_index('Attribute',inplace=True)
df.transpose()

Or

df.set_index('Attribute').T

Transpose DataFrame in Pandas while preserving Index column

If I understand your example, what seems to happen to you is that you transpose takes your actual index (the 0...n sequence as column headers. First, if you then want to preserve the numerical index, you can store that as id2.

DF['id2'] = DF.index

Now if you want id to be the column headers then you must set that as an index, overriding the default one:

DF.set_index('id',inplace=True)
DF.T

I don't have your data reproduced, but this should give you the values of id across columns.

How do I transpose a pandas dataframe while preserving the index and rename keys

I was able to do it another way. Here's my code. After transposing:

df.columns = df.iloc[0]
df = df.reset_index()
df = df.drop(df.index[0])
df = df.reset_index()
df =df.drop(["level_0","index"],axis = 1)

Properly transpose Pandas dataframe and access first column

Set fruit as the index; selection should be much easier:

temp =  df.set_index('fruit')
temp.loc['weight', 'apple']
50

Pandas: After using transpose old index name does not vanish when using dataFrame.index.set_names

First question. Why is country now the index for 1800, 1801 etc. and is there a better tranpose option to avoid this?

'country' is the name/ label of the column axis since it was the name of the index of the DataFrame before transposing.

df_2.index.set_names(["year"],inplace=True)

Question two. Why is country still there and how to remove it?

Because you are only changing the index name of the transposed DataFrame. The name of the column axis ('country') is left unchanged.

How to remove it:

You can use DataFrame.rename_axis to change the name of both axes (index and column) at the same time. If you want to remove the name of a given axis, just pass None.

For instance,

# or df_2 = df1.T.rename_axis(index='year', columns=None) if you prefer
>>> df_2 = df_1.rename_axis(index=None, columns='year').T
>>> df_2

      Germany
year         
1800     38.4
1801     38.4
1802     38.4

Transposing all but the first column in a dataframe

When transposing, the index becomes columns and the columns index, so you need to first set ISIN as the index and then transpose.

import pandas as pd

df = pd.DataFrame({'ISIN': ['A', 'B', 'C'], 
                   'Jan': [40000, 50000, 42000],
                   'Feb': [40000, 50000, 42000],
                   'Mar': [40000, 50000, 42000]})
df.set_index('ISIN', inplace=True)
print(df.T)

Which produces:

ISIN      A      B      C
Jan   40000  50000  42000
Feb   40000  50000  42000
Mar   40000  50000  42000

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