import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import stats
sns.set(style="white", color_codes=True)

df = pd.read_csv('video game sales 2016-12-22.csv', parse_dates=['Year_of_Release'])

df.shape
df.info
df.dtypes

df['User_Score'] = pd.to_numeric(df['User_Score'], downcast = 'integer', errors = 'coerce')

pc = df[df['Platform'] == 'PC']

sns.jointplot(x = 'Critic_Score', y = 'User_Score', data = pc, kind = 'reg')

df.groupby('Genre')[['NA_Sales', 'JP_Sales', 'EU_Sales']].sum().sort_values('NA_Sales', ascending = False).plot(kind = 'bar')

618b7c32d4a9f708533d53e8fe9a0bde6ab70be1
<mask token>

<mask token>
sns.set(style='white', color_codes=True)
<mask token>
df.shape
df.info
df.dtypes
<mask token>
sns.jointplot(x='Critic_Score', y='User_Score', data=pc, kind='reg')
df.groupby('Genre')[['NA_Sales', 'JP_Sales', 'EU_Sales']].sum().sort_values(
    'NA_Sales', ascending=False).plot(kind='bar')

<mask token>
sns.set(style='white', color_codes=True)
df = pd.read_csv('video game sales 2016-12-22.csv', parse_dates=[
    'Year_of_Release'])
df.shape
df.info
df.dtypes
df['User_Score'] = pd.to_numeric(df['User_Score'], downcast='integer',
    errors='coerce')
pc = df[df['Platform'] == 'PC']
sns.jointplot(x='Critic_Score', y='User_Score', data=pc, kind='reg')
df.groupby('Genre')[['NA_Sales', 'JP_Sales', 'EU_Sales']].sum().sort_values(
    'NA_Sales', ascending=False).plot(kind='bar')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import stats
sns.set(style='white', color_codes=True)
df = pd.read_csv('video game sales 2016-12-22.csv', parse_dates=[
    'Year_of_Release'])
df.shape
df.info
df.dtypes
df['User_Score'] = pd.to_numeric(df['User_Score'], downcast='integer',
    errors='coerce')
pc = df[df['Platform'] == 'PC']
sns.jointplot(x='Critic_Score', y='User_Score', data=pc, kind='reg')
df.groupby('Genre')[['NA_Sales', 'JP_Sales', 'EU_Sales']].sum().sort_values(
    'NA_Sales', ascending=False).plot(kind='bar')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import stats
sns.set(style="white", color_codes=True)

df = pd.read_csv('video game sales 2016-12-22.csv', parse_dates=['Year_of_Release'])

df.shape
df.info
df.dtypes

df['User_Score'] = pd.to_numeric(df['User_Score'], downcast = 'integer', errors = 'coerce')

pc = df[df['Platform'] == 'PC']

sns.jointplot(x = 'Critic_Score', y = 'User_Score', data = pc, kind = 'reg')

df.groupby('Genre')[['NA_Sales', 'JP_Sales', 'EU_Sales']].sum().sort_values('NA_Sales', ascending = False).plot(kind = 'bar')
