Not sure how to fix/fill empty Data Frame in Python.  Importing data from csv.
Hi,

I am trying to group this one column that is for 1-11 regions as categorical variable and then print the mean for each group. My data column is 'Alcohol' and region column to groupby is 'REGION' but I keep getting error.  Here is code.  I need help as I am new to programming.

```
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.stats.multicomp as multi

data = pd.read_csv('gapminder2.csv', low_memory=False)

#setting variables you will be working with to numeric
data['ALCOHOL'] = data['alcconsumption'].convert_objects(convert_numeric=True)
data['REGION'] = data['country2'].convert_objects(convert_numeric=True)

ALCOHOL = data['ALCOHOL']

meanTest = pd.DataFrame(columns = ['ALCOHOL', 'REGION']).astype(float)
    
print ('mean per region')
mean = meanTest.groupby('REGION').mean()
print (mean)
```

Here is my error:
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
mean per region
Empty DataFrame
Columns: [ALCOHOL]
Index: []
C:/Users/blahblahblah/Documents/untitled1.py:15: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
data['ALCOHOL'] = data['alcconsumption'].convert_objects(convert_numeric=True)
C:/Users/C5182508/OneDrive - SAP SE/Documents/untitled1.py:16: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
data['REGION'] = data['country2'].convert_objects(convert_numeric=True)

Here is picture:
(https://cloud.githubusercontent.com/assets/26236052/23641223/7ad06c26-02af-11e7-8811-52cb3d1b8f2c.png)
