<jupyter_start><jupyter_text>Serias<jupyter_code>import pandas as pd

data = pd.Series(["Январь", "Февраль", "Март", "Апрель"],
                 index = ['Первый', "Второй", "Третий", "Четвёртый"])
display(data)
data.loc["Первый"]
data.loc[["Первый", "Третий"]]
data = pd.Series(list(range(10, 1001)))
data
print(data.loc[10] + data.loc[23] - data.loc[245] + data.iloc[122])<jupyter_output>-70<jupyter_text>DataFrame<jupyter_code>df = pd.DataFrame([ [1,2,3], [2,3,4] ],
                  columns = ['foo', 'bar', 'baz'], 
                  index = ['foobar', 'foobaz'])
df
pd.DataFrame({'Тестовая колонка':[1,5]})
import pandas as pd

football = pd.read_csv('data/data_sf.csv', index_col=[0])
# display(football.head())
football.head()
import pandas as pd

football = pd.read_csv('data/data_sf.csv')
display(football.info())
football.info(null_counts=True)
football.describe()
football.describe(include=['object'])
football['Age'].mean()
football[['Composure']].info()
football['ShortPassing'].describe()
football['Wage'].sum()
football['Value'].min()
football['Wage'].mean()
football[football['Wage'] > football['Wage'].mean()]['SprintSpeed'].mean()
football[football['Wage'] < football['Wage'].mean()]['SprintSpeed'].mean()
football[football['Wage'] == football['Wage'].max()]['Position']
football[football['Nationality'] == 'Brazil']['Penalties'].sum()
football[football['HeadingAccuracy'] > 50 ]['Age'].mean()
football[(football['Composure'] > football['Composure'] * 0.9) & 
         (football['Reactions'] > football['Reactions'] * 0.9)]['Age'].min()
football[(football['Composure'] > football['Composure'].max() * 0.9) & 
         (football['Reactions'] > football['Reactions'].max() * 0.9)]['Age'].min()
football[football['Age'] == football['Age'].max()]['Reactions'].mean() -\
football[football['Age'] == football['Age'].min()]['Reactions'].mean()
football[football['Value'] > football['Value'].mean()]['Nationality'].value_counts()
football[(football['Position'] == 'GK') & (football['GKReflexes'] == football['GKReflexes'].max())]['Wage'].max() /\
football[(football['Position'] == 'GK') & (football['GKHandling'] == football['GKHandling'].max())]['Wage'].max()
football[football['Aggression'] == football['Aggression'].max()]['ShotPower'].mean() /\
football[football['Aggression'] == football['Aggression'].min()]['ShotPower'].mean()<jupyter_output><empty_output>
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