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data_df = googletrends_queries(keywords=keywords, geo=geo, loops=2)

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def googletrends_queries(keywords, geo, loops=3, wait=10, csv='trends.csv'):
    # Perform searches
    print('Number of queries to do: ', len(keywords) * len(geo))

    # Prepare containers
    trends = dict.fromkeys(geo)
    errors_list = []
    cnt = 1

    # Start loops

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dict_of_trends = {g: pd.DataFrame(k) for g,k in trends.items()}
data_df = pd.concat(dict_of_trends, axis=1)
data_df.to_csv('trends.csv',index=False)
data_df.head() # Check data

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# Redo searches with errors
if len(errors_list) > 0:
    print('\nStart errors:')

    for t in range(1,loops+1):

        if len(errors_list) > 0:
            print('Loop ', t, ': ', len(errors_list), 'errors')

            for i in errors_list:

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# Perform searches
wait = 6 # in seconds
print('Number of queries to do: ', len(keywords) * len(geo))

# Prepare containers
trends = dict.fromkeys(geo)
errors_list = []
cnt = 1

# Start loops

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# keywords
keywords = ['apple', 'facebook', 'bitcoin']
keywords.sort() # Sort the list

# locations
geo = ['US', 'AU', 'GB', 'DE']
geo.sort()

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from pytrends.request import TrendReq
pytrends = TrendReq(hl='en-US', tz=0, retries=10)

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import time
import pandas as pd   
import pytrends