Python Data Analysis (пару строчек кода )

import imp, os, sys

sol = imp.load_compiled("solutions", "./solutions.py")

sol.get_solutions("imdb.xlsx")

from nose.tools import assert_equal

from pandas.util.testing import assert_frame_equal, assert_series_equal

# Loading the data

import pandas as pd

xls = pd.ExcelFile('imdb.xlsx')

df = xls.parse('imdb')

df_directors = xls.parse('directors')

df_countries = xls.parse('countries')

print("Data Loading Finished.")

Задание 1:

Who is the director with the most movies? First get the number of movies per "director_name", then save the director's name

and count as a series of length 1 called "director_with_most"

"""

# your code here

director_with_most = df.director_name.value_counts().loc[lambda x: x == x.max()]

Save all of this director's movies and their ratings in a variable called all_movies_ratings, then print this variable.

(The director with the most movies you got from the last question.)

"""

# your code here

Не знаю как сделать этот блок .

2. Задание .

import imp, os, sys

sol = imp.load_compiled("solutions", "./solutions.py")

sol.get_solutions("imdb.xlsx")

from nose.tools import assert_equal

from pandas.util.testing import assert_frame_equal, assert_series_equal

# Loading the data

import pandas as pd

xls = pd.ExcelFile('imdb.xlsx')

df = xls.parse('imdb')

df_directors = xls.parse('directors')

df_countries = xls.parse('countries')

df = pd.merge(left=df, right=df_countries, 

              how='inner', left_on='country_id', 

              right_on='id')

df = pd.merge(left=df, right=df_directors, 

              how='inner', left_on='director_id', 

              right_on='id')

print("Finished.")

Create a series called 'directors' that contains each director's name and his or her average rating.  Print out the type of your variable.

Use the 'directors' series to find the average rating for Steven Spielberg.  Print the value.

"""

# your code here

...

Create a Pivot Table that shows the median rating for each director, grouped by their respective countries. Name your variable

'pivot_agg'

# your code here

....

3. Задание .

import imp, os, sys

sol = imp.load_compiled("solutions", "./solutions.py")

sol.get_solutions("imdb.xlsx")

from nose.tools import assert_equal

from pandas.util.testing import assert_frame_equal, assert_series_equal

# Loading the data

import pandas as pd

import numpy as np

xls = pd.ExcelFile('imdb.xlsx')

df = xls.parse('imdb')

df_directors = xls.parse('directors')

df_countries = xls.parse('countries')

df = pd.merge(left=df, right=df_countries, 

              how='inner', left_on='country_id', 

              right_on='id')

df = pd.merge(left=df, right=df_directors, 

              how='inner', left_on='director_id', 

              right_on='id')

print("Finished.")

Is how much a movie makes indicative of how good it is?

Make a simple scatter plot comparing gross to imdb_score for movies during or after 2000 (title_year >= 2000) and before 2000 (title_year < 2000).

It may be useful to scale the x axis demarking gross. (Hint: Divide the gross amount by 1,000,000.)

Remember to put a legend indicating which color corresponds to which years.

What is your verdict?

Save your plot in a variable called plt1, and your dataframes in variables called df_after_2000 and df_before_2000

"""

import matplotlib.pyplot as plt1

# your code here

...

Using numpy and pyplot, make an overlapping histogram that shows the score distribution vs. count of R-Rated movies and PG-13 ones.

Describe your plot. 

Save your plot in a variable called plt2, and your dataframes in variables called df_R and df_PG13

"""

import matplotlib.pyplot as plt2

# your code here

...