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.

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

Function explicitly returns two DataFrames:

import pandas as pd
import numpy as np

def return_2DF():

    date = pd.date_range('today', periods=20)
    DF1 = pd.DataFrame(np.random.rand(20, 2), index=date, columns=list('xyz'))

    DF2 = pd.DataFrame(np.random.rand(20, 4), index=date, columns='A B C D'.split())

    return DF1, DF2

Calling and returning two data frame

one, two = return_2DF()

Dataframe_object.copy()

A deep copy needs to be performed to avoid issues of one dataframe being the reference to another dataframe. This is most crucial when you have a function in a module (or a separate file) returning a dataframe. If you don't do return DataFrame_object.copy(), it will only return a reference to the dataframe created in the function.\

If you are using a function in the same file, you might not even realize this issue of deep copy / shallow copy if you are using a global variable in the function.

I have come across this issue before but solved it really easily by setting a variable outside the function to be the output of the function.

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

df = create_df()