saeedesmaili

import pandas as pd

df = pd.read_csv('rides.csv')

# Hourly
df_tehran = df.copy()[df.city == "Tehran"]
df_tehran['timestamp'] = pd.to_datetime(df_tehran['timestamp'])
df_tehran_g = df_tehran.groupby([df_tehran.timestamp.dt.hour, df_tehran.service], as_index=True).agg({
    'price_per_km': 'mean'
})

saeedesmaili

server = 'your_sql_server_address'
database = 'database_name'
username = 'your_username'
password = 'your_password'

saeedesmaili

import pandas as pd

users_columns = ['user_id', 'gender', 'age', 't', 'zip']
df_users = pd.read_table('users.dat', sep='::', header=None, names=users_columns, engine='python')

ratings_columns = ['user_id', 'movie_id', 'rating', 'timestamp']
df_ratings = pd.read_table('ratings.dat', sep='::', header=None, names=ratings_columns, engine='python')

movies_columns = ['movie_id', 'title', 'genres']
df_movies = pd.read_table('movies.dat', sep='::', header=None, names=movies_columns, engine='python')