Constructing pandas DataFrame from values in variables gives "ValueError: If using all scalar values, you must pass an index"

This may be a simple question, but I can not figure out how to do this. Lets say that I have two variables as follows.

a = 2
b = 3

I want to construct a DataFrame from this:

df2 = pd.DataFrame({'A':a,'B':b})

This generates an error:

ValueError: If using all scalar values, you must pass an index

I tried this also:

df2 = (pd.DataFrame({'a':a,'b':b})).reset_index()

This gives the same error message.

Solution 1:

The error message says that if you're passing scalar values, you have to pass an index. So you can either not use scalar values for the columns -- e.g. use a list:

>>> df = pd.DataFrame({'A': [a], 'B': [b]})
>>> df
   A  B
0  2  3

or use scalar values and pass an index:

>>> df = pd.DataFrame({'A': a, 'B': b}, index=[0])
>>> df
   A  B
0  2  3

Solution 2:

You can also use pd.DataFrame.from_records which is more convenient when you already have the dictionary in hand:

df = pd.DataFrame.from_records([{ 'A':a,'B':b }])

You can also set index, if you want, by:

df = pd.DataFrame.from_records([{ 'A':a,'B':b }], index='A')

Solution 3:

You may try wrapping your dictionary into a list:

my_dict = {'A':1,'B':2}
pd.DataFrame([my_dict])

   A  B
0  1  2

Solution 4:

You need to create a pandas series first. The second step is to convert the pandas series to pandas dataframe.

import pandas as pd
data = {'a': 1, 'b': 2}
pd.Series(data).to_frame()

You can even provide a column name.

pd.Series(data).to_frame('ColumnName')

Solution 5:

Maybe Series would provide all the functions you need:

pd.Series({'A':a,'B':b})

DataFrame can be thought of as a collection of Series hence you can :

Concatenate multiple Series into one data frame (as described here )
Add a Series variable into existing data frame ( example here )