Returning a dataframe in python function

I'm kind of late here, but what about creating a global variable within the function? It should save a step for you.

def create_df():

    global df

    data = {
    'state': ['Ohio','Ohio','Ohio','Nevada','Nevada'],
    'year': [2000,2001,2002,2001,2002],
    'pop': [1.5,1.7,3.6,2.4,2.9]
    }

    df = pd.DataFrame(data)

Then when you run create_df(), you'll be able to just use df.

Of course, be careful in your naming strategy if you have a large program so that the value of df doesn't change as various functions execute.

EDIT: I noticed I got some points for this. Here's another (probably worse) way to do this using exec. This also allows for multiple dataframes to be created, if desired.

import pandas as pd

def create_df():
    data = {'state': ['Ohio','Ohio','Ohio','Nevada','Nevada'],
           'year': [2000,2001,2002,2001,2002],
           'pop': [1.5,1.7,3.6,2.4,2.9]}
    df = pd.DataFrame(data)
    return df

### We'll create three dataframes for an example
for i in range(3):
    exec(f'df_{i} = create_df()')

Then, you can test them out:

Input: df_0

Output:

    state  year  pop
0    Ohio  2000  1.5
1    Ohio  2001  1.7
2    Ohio  2002  3.6
3  Nevada  2001  2.4
4  Nevada  2002  2.9

Input: df_1

Output:

    state  year  pop
0    Ohio  2000  1.5
1    Ohio  2001  1.7
2    Ohio  2002  3.6
3  Nevada  2001  2.4
4  Nevada  2002  2.9

Etc.

Wwhen you call create_df(), Python calls the function but doesn't save the result in any variable. That is why you got the error.

Assign the result of create_df() to a new variable df like this:

df = create_df()
df

You can return dataframe from a function by making a copy of the dataframe like

def my_function(dataframe):
  my_df=dataframe.copy()
  my_df=my_df.drop(0)
  return(my_df)

new_df=my_function(old_df)
print(type(new_df))

Output: pandas.core.frame.DataFrame