import pandas as pd

pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})

pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']})

pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 
              'Sue': ['Pretty good.', 'Bland.']},
             index=['Product A', 'Product B'])

pd.Series([1, 2, 3, 4, 5])

pd.Series([30, 35, 40], index=['2015 Sales', '2016 Sales', '2017 Sales'], name='Product A')

wine_reviews = pd.read_csv("F:\\kaggleDataSet\\kepler-exoplanet-search-results\\winemag-data-130k-v2.csv")

wine_reviews.shape

wine_reviews.head()

wine_reviews = pd.read_csv("F:\\kaggleDataSet\\kepler-exoplanet-search-results\\winemag-data-130k-v2.csv", index_col=0)
wine_reviews.head()

import sqlite3
conn = sqlite3.connect("F:\\kaggleDataSet\\kepler-exoplanet-search-results\\FPA_FOD_20170508.sqlite")

fires = pd.read_sql_query("SELECT * FROM fires", conn)

fires.head()

wine_reviews.head().to_csv("F:\\kaggleDataSet\\kepler-exoplanet-search-results\\wine_reviews.csv")

conn = sqlite3.connect("F:\\kaggleDataSet\\kepler-exoplanet-search-results\\fires.sqlite")
fires.head(10).to_sql("fires", conn)