import numpy as np
x = np.array([1, 2, 3, 4, 5])
x[x > 2] = 12
x
array([ 1, 2, 12, 12, 12])
True + True
2
False == 0
True
x = np.array([1, 2, 3, 2, 1])
(x == 1).nonzero()
(array([0, 4]),)
(np.array([[1, 2, 3], [2, 3, 2]]) == 2).nonzero()
(array([0, 1, 1]), array([1, 0, 2]))
x = np.array([1, 2, 3, 4, 5, 6])
s = x[1:3]
s
array([2, 3])
s.sum()
5
np.sum(s)
5
s.mean()
2.5
import pandas as pd
df = pd.DataFrame([[1, 2, 3], [2, 3, 4]])
df
0 | 1 | 2 | |
---|---|---|---|
0 | 1 | 2 | 3 |
1 | 2 | 3 | 4 |
grades = pd.DataFrame([[3, 4],
[4, 3],
[5, 3]],
index=['Alice', 'Bob', 'Claudia'],
columns=['Algebra', 'Geometry'])
grades
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
Claudia | 5 | 3 |
grades['Algebra']
# grades['Alice']
Alice 3 Bob 4 Claudia 5 Name: Algebra, dtype: int64
grades['Algebra':'Geometry']
# grades['Alice':'Bob']
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
Claudia | 5 | 3 |
grades['Alice':'Bob']
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
grades[:2]
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
grades['Geometry']
Alice 4 Bob 3 Claudia 3 Name: Geometry, dtype: int64
grades[1:3]
Algebra | Geometry | |
---|---|---|
Bob | 4 | 3 |
Claudia | 5 | 3 |
grades[0]
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2896 try: -> 2897 return self._engine.get_loc(key) 2898 except KeyError: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 0 During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) <ipython-input-36-b0ec7979780f> in <module>() ----> 1 grades[0] /usr/local/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key) 2993 if self.columns.nlevels > 1: 2994 return self._getitem_multilevel(key) -> 2995 indexer = self.columns.get_loc(key) 2996 if is_integer(indexer): 2997 indexer = [indexer] /usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2897 return self._engine.get_loc(key) 2898 except KeyError: -> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key)) 2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance) 2901 if indexer.ndim > 1 or indexer.size > 1: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 0
grades.loc['Alice']
Algebra 3 Geometry 4 Name: Alice, dtype: int64
grades.loc[:, 'Algebra']
Alice 3 Bob 4 Claudia 5 Name: Algebra, dtype: int64
grades.loc[:, 'Algebra':'Geometry']
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
Claudia | 5 | 3 |
grades.loc[1]
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-43-6ba2b639a084> in <module>() ----> 1 grades.loc[1] /usr/local/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key) 1422 1423 maybe_callable = com.apply_if_callable(key, self.obj) -> 1424 return self._getitem_axis(maybe_callable, axis=axis) 1425 1426 def _is_scalar_access(self, key: Tuple): /usr/local/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis) 1847 1848 # fall thru to straight lookup -> 1849 self._validate_key(key, axis) 1850 return self._get_label(key, axis=axis) 1851 /usr/local/lib/python3.7/site-packages/pandas/core/indexing.py in _validate_key(self, key, axis) 1723 1724 if not is_list_like_indexer(key): -> 1725 self._convert_scalar_indexer(key, axis) 1726 1727 def _is_scalar_access(self, key: Tuple): /usr/local/lib/python3.7/site-packages/pandas/core/indexing.py in _convert_scalar_indexer(self, key, axis) 272 ax = self.obj._get_axis(min(axis, self.ndim - 1)) 273 # a scalar --> 274 return ax._convert_scalar_indexer(key, kind=self.name) 275 276 def _convert_slice_indexer(self, key, axis: int): /usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in _convert_scalar_indexer(self, key, kind) 3136 elif kind in ["loc"] and is_integer(key): 3137 if not self.holds_integer(): -> 3138 return self._invalid_indexer("label", key) 3139 3140 return key /usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in _invalid_indexer(self, form, key) 3338 "cannot do {form} indexing on {klass} with these " 3339 "indexers [{key}] of {kind}".format( -> 3340 form=form, klass=type(self), key=key, kind=type(key) 3341 ) 3342 ) TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [1] of <class 'int'>
grades.iloc[1]
Algebra 4 Geometry 3 Name: Bob, dtype: int64
grades.iloc[:, 1]
Alice 4 Bob 3 Claudia 3 Name: Geometry, dtype: int64
grades.iloc[1, 1]
3
grades.loc['Alice', 'Algebra']
3
x = "Alice"
grades.loc[f"{x}"]
Algebra 3 Geometry 4 Name: Alice, dtype: int64
grades.iloc[1+1]
Algebra 5 Geometry 3 Name: Claudia, dtype: int64
grades[grades.mean(axis=1) > 3.5]
Algebra | Geometry | |
---|---|---|
Claudia | 5 | 3 |
grades.loc[grades.mean(axis=1) > 3.5]
Algebra | Geometry | |
---|---|---|
Claudia | 5 | 3 |
grades
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 3 |
Claudia | 5 | 3 |
grades.iloc[1,1] = 5
grades
Algebra | Geometry | |
---|---|---|
Alice | 3 | 4 |
Bob | 4 | 5 |
Claudia | 5 | 3 |
grades['Calculus'] = [2, 3, 2]
grades.T
Alice | Bob | Claudia | |
---|---|---|---|
Algebra | 3 | 4 | 5 |
Geometry | 4 | 5 | 3 |
Calculus | 2 | 3 | 2 |
grades.loc['Daniel'] = [2, 3, 5]
grades.sort_index(axis=1)
Algebra | Calculus | Geometry | |
---|---|---|---|
Alice | 3 | 2 | 4 |
Bob | 4 | 3 | 5 |
Claudia | 5 | 2 | 3 |
Daniel | 2 | 5 | 3 |
grades
Algebra | Geometry | Calculus | |
---|---|---|---|
Alice | 3 | 4 | 2 |
Bob | 4 | 5 | 3 |
Claudia | 5 | 3 | 2 |
Daniel | 2 | 3 | 5 |
grades.sort_index(axis=1, inplace=True)
grades
Algebra | Calculus | Geometry | |
---|---|---|---|
Alice | 3 | 2 | 4 |
Bob | 4 | 3 | 5 |
Claudia | 5 | 2 | 3 |
Daniel | 2 | 5 | 3 |
grades.sort_values('Algebra', ascending=False)
Algebra | Calculus | Geometry | |
---|---|---|---|
Claudia | 5 | 2 | 3 |
Bob | 4 | 3 | 5 |
Alice | 3 | 2 | 4 |
Daniel | 2 | 5 | 3 |
grades.sort_values(['Calculus', 'Algebra'],
ascending=[False, True])
Algebra | Calculus | Geometry | |
---|---|---|---|
Daniel | 2 | 5 | 3 |
Bob | 4 | 3 | 5 |
Alice | 3 | 2 | 4 |
Claudia | 5 | 2 | 3 |
grades.loc[['Alice', 'Claudia']]
Algebra | Calculus | Geometry | |
---|---|---|---|
Alice | 3 | 2 | 4 |
Claudia | 5 | 2 | 3 |
grades['mean'] = grades.mean(axis=1)
grades.sort_values('mean')
Algebra | Calculus | Geometry | mean | |
---|---|---|---|---|
Alice | 3 | 2 | 4 | 3.000000 |
Claudia | 5 | 2 | 3 | 3.333333 |
Daniel | 2 | 5 | 3 | 3.333333 |
Bob | 4 | 3 | 5 | 4.000000 |
grades.drop('mean', axis=1)
Algebra | Calculus | Geometry | |
---|---|---|---|
Alice | 3 | 2 | 4 |
Bob | 4 | 3 | 5 |
Claudia | 5 | 2 | 3 |
Daniel | 2 | 5 | 3 |
grades
Algebra | Calculus | Geometry | mean | |
---|---|---|---|---|
Alice | 3 | 2 | 4 | 3.000000 |
Bob | 4 | 3 | 5 | 4.000000 |
Claudia | 5 | 2 | 3 | 3.333333 |
Daniel | 2 | 5 | 3 | 3.333333 |
grades.drop('mean', axis=1, inplace=True)
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-98-9f14b63b756d> in <module>() ----> 1 grades.drop('mean', axis=1, inplace=True) /usr/local/lib/python3.7/site-packages/pandas/core/frame.py in drop(self, labels, axis, index, columns, level, inplace, errors) 4115 level=level, 4116 inplace=inplace, -> 4117 errors=errors, 4118 ) 4119 /usr/local/lib/python3.7/site-packages/pandas/core/generic.py in drop(self, labels, axis, index, columns, level, inplace, errors) 3912 for axis, labels in axes.items(): 3913 if labels is not None: -> 3914 obj = obj._drop_axis(labels, axis, level=level, errors=errors) 3915 3916 if inplace: /usr/local/lib/python3.7/site-packages/pandas/core/generic.py in _drop_axis(self, labels, axis, level, errors) 3944 new_axis = axis.drop(labels, level=level, errors=errors) 3945 else: -> 3946 new_axis = axis.drop(labels, errors=errors) 3947 result = self.reindex(**{axis_name: new_axis}) 3948 /usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in drop(self, labels, errors) 5338 if mask.any(): 5339 if errors != "ignore": -> 5340 raise KeyError("{} not found in axis".format(labels[mask])) 5341 indexer = indexer[~mask] 5342 return self.delete(indexer) KeyError: "['mean'] not found in axis"
(grades
.assign(mean=grades.mean(axis=1))
.sort_values('mean')[:2]
)
Algebra | Calculus | Geometry | mean | |
---|---|---|---|---|
Alice | 3 | 2 | 4 | 3.000000 |
Claudia | 5 | 2 | 3 | 3.333333 |
(grades
.assign(mean=grades.mean(axis=1))
.sort_values('mean')[:2].index[0]
)
'Alice'
df = pd.read_csv("https://bit.ly/2VbRAty")
url = "https://github.com/Godoy/imdb-5000-movie-dataset/raw/master/data/movie_metadata.csv"
df = pd.read_csv(url)
df['color'].unique()
array(['Color', nan, ' Black and White'], dtype=object)
df[df['color'] == ' Black and White'].iloc[0]
color Black and White director_name Michael Bay num_critic_for_reviews 191 duration 184 director_facebook_likes 0 actor_3_facebook_likes 691 actor_2_name Jaime King actor_1_facebook_likes 3000 gross 1.9854e+08 genres Action|Drama|History|Romance|War actor_1_name Jennifer Garner movie_title Pearl Harbor num_voted_users 254111 cast_total_facebook_likes 5401 actor_3_name Mako facenumber_in_poster 0 plot_keywords air raid|black smoke|japanese military|japanes... movie_imdb_link http://www.imdb.com/title/tt0213149/?ref_=fn_t... num_user_for_reviews 1999 language English country USA content_rating PG-13 budget 1.4e+08 title_year 2001 actor_2_facebook_likes 961 imdb_score 6.1 aspect_ratio 2.35 movie_facebook_likes 0 Name: 111, dtype: object
df['color']
0 Color 1 Color 2 Color 3 Color 4 NaN ... 5038 Color 5039 Color 5040 Color 5041 Color 5042 Color Name: color, Length: 5043, dtype: object
df.color.value_counts(dropna=False)
Color 4815 Black and White 209 NaN 19 Name: color, dtype: int64
list(df['director_name'][:10])
['James Cameron', 'Gore Verbinski', 'Sam Mendes', 'Christopher Nolan', 'Doug Walker', 'Andrew Stanton', 'Sam Raimi', 'Nathan Greno', 'Joss Whedon', 'David Yates']
df.sort_values('imdb_score')
2834 Justin Bieber: Never Say Never 1136 Foodfight! 2295 Superbabies: Baby Geniuses 2 4605 The Helix... Loaded 2268 Disaster Movie ... 4409 Kickboxer: Vengeance 3207 Dekalog 3466 The Godfather 1937 The Shawshank Redemption 2765 Towering Inferno Name: movie_title, Length: 5043, dtype: object
df.columns
Index(['color', 'director_name', 'num_critic_for_reviews', 'duration', 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name', 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name', 'movie_title', 'num_voted_users', 'cast_total_facebook_likes', 'actor_3_name', 'facenumber_in_poster', 'plot_keywords', 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country', 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes', 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'], dtype='object')
(df
.groupby('country')
['imdb_score']
.mean()
.sort_values(ascending=False))[:10].index
Index(['Kyrgyzstan', 'Libya', 'United Arab Emirates', 'Soviet Union', 'Egypt', 'Iran', 'Poland', 'Indonesia', 'Israel', 'Sweden'], dtype='object', name='country')
df.loc[df['country'] == 'Kyrgyzstan', 'movie_title']
4468 Queen of the Mountains Name: movie_title, dtype: object
g = df.groupby('country')
(df
.groupby('country')
.max())
num_critic_for_reviews | duration | director_facebook_likes | actor_3_facebook_likes | actor_1_facebook_likes | gross | genres | movie_title | num_voted_users | cast_total_facebook_likes | facenumber_in_poster | movie_imdb_link | num_user_for_reviews | budget | title_year | actor_2_facebook_likes | imdb_score | aspect_ratio | movie_facebook_likes | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
country | |||||||||||||||||||
Afghanistan | 105.0 | 83.0 | 6.0 | 0.0 | 30.0 | 1127331.0 | Drama | Osama | 7559 | 30 | 1.0 | http://www.imdb.com/title/tt0368913/?ref_=fn_t... | 77.0 | 46000.0 | 2003.0 | 0.0 | 7.4 | 1.85 | 0 |
Argentina | 262.0 | 129.0 | 195.0 | 50.0 | 827.0 | 20167424.0 | Drama|Mystery|Thriller | The Secret in Their Eyes | 131831 | 1044 | 0.0 | http://www.imdb.com/title/tt1305806/?ref_=fn_t... | 231.0 | 2000000.0 | 2009.0 | 88.0 | 8.2 | 2.35 | 33000 |
Aruba | 67.0 | 91.0 | 85.0 | 105.0 | 635.0 | 10076136.0 | Action|Comedy|Thriller | Knock Off | 11512 | 1352 | 0.0 | http://www.imdb.com/title/tt0120724/?ref_=fn_t... | 141.0 | 35000000.0 | 1998.0 | 316.0 | 4.8 | 2.35 | 471 |
Australia | 739.0 | 197.0 | 1000.0 | 2000.0 | 49000.0 | 257756197.0 | Mystery|Thriller | Wolf Creek | 552503 | 62644 | 12.0 | http://www.imdb.com/title/tt4460878/?ref_=fn_t... | 2121.0 | 150000000.0 | 2015.0 | 19000.0 | 8.1 | 2.39 | 191000 |
Bahamas | 64.0 | 94.0 | 261.0 | 179.0 | 12000.0 | NaN | Drama|Thriller | Dying of the Light | 6964 | 12676 | 1.0 | http://www.imdb.com/title/tt1274586/?ref_=fn_t... | 47.0 | 5000000.0 | 2014.0 | 316.0 | 4.4 | 2.35 | 0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Turkey | 16.0 | 122.0 | 11.0 | 173.0 | 205.0 | NaN | Action|Adventure | Valley of the Wolves: Iraq | 14486 | 808 | 3.0 | http://www.imdb.com/title/tt0493264/?ref_=fn_t... | 159.0 | 8300000.0 | 2006.0 | 197.0 | 6.0 | 1.85 | 467 |
UK | 750.0 | 251.0 | 14000.0 | 19000.0 | 49000.0 | 362645141.0 | Western | You Only Live Twice | 641997 | 77823 | 9.0 | http://www.imdb.com/title/tt5116280/?ref_=fn_t... | 2301.0 | 250000000.0 | 2016.0 | 22000.0 | 8.6 | 16.00 | 165000 |
USA | 813.0 | 330.0 | 23000.0 | 23000.0 | 640000.0 | 760505847.0 | Western | Æon Flux | 1689764 | 656730 | 43.0 | http://www.imdb.com/title/tt5574490/?ref_=fn_t... | 4667.0 | 300000000.0 | 2016.0 | 137000.0 | 9.3 | 16.00 | 349000 |
United Arab Emirates | NaN | 62.0 | 58.0 | NaN | NaN | NaN | Documentary|Family | The Brain That Sings | 18 | 0 | 1.0 | http://www.imdb.com/title/tt2638024/?ref_=fn_t... | NaN | 125000.0 | 2013.0 | NaN | 8.2 | NaN | 54 |
West Germany | 99.0 | 293.0 | 249.0 | 271.0 | 16000.0 | 11433134.0 | Horror|Mystery | The Torture Chamber of Dr. Sadism | 168203 | 16110 | 1.0 | http://www.imdb.com/title/tt0088323/?ref_=fn_t... | 426.0 | 27000000.0 | 1984.0 | 312.0 | 8.4 | 2.35 | 21000 |
65 rows × 19 columns
(df
.groupby('country')
.max()
.sort_values('imdb_score', ascending=False))[:10].index
Index(['Canada', 'USA', 'Poland', 'Italy', 'New Zealand', 'Japan', 'Brazil', 'Kyrgyzstan', 'UK', 'France'], dtype='object', name='country')
df[['color']]
color | |
---|---|
0 | Color |
1 | Color |
2 | Color |
3 | Color |
4 | NaN |
... | ... |
5038 | Color |
5039 | Color |
5040 | Color |
5041 | Color |
5042 | Color |
5043 rows × 1 columns
df.gross / df.budget
0 3.208885 1 1.031347 2 0.816629 3 1.792523 4 NaN ... 5038 NaN 5039 NaN 5040 NaN 5041 NaN 5042 77.474545 Length: 5043, dtype: float64
df.assign(budget_delta=df.gross / df.budget)
color | director_name | num_critic_for_reviews | duration | director_facebook_likes | actor_3_facebook_likes | actor_2_name | actor_1_facebook_likes | gross | genres | ... | language | country | content_rating | budget | title_year | actor_2_facebook_likes | imdb_score | aspect_ratio | movie_facebook_likes | budget_delta | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Color | James Cameron | 723.0 | 178.0 | 0.0 | 855.0 | Joel David Moore | 1000.0 | 760505847.0 | Action|Adventure|Fantasy|Sci-Fi | ... | English | USA | PG-13 | 237000000.0 | 2009.0 | 936.0 | 7.9 | 1.78 | 33000 | 3.208885 |
1 | Color | Gore Verbinski | 302.0 | 169.0 | 563.0 | 1000.0 | Orlando Bloom | 40000.0 | 309404152.0 | Action|Adventure|Fantasy | ... | English | USA | PG-13 | 300000000.0 | 2007.0 | 5000.0 | 7.1 | 2.35 | 0 | 1.031347 |
2 | Color | Sam Mendes | 602.0 | 148.0 | 0.0 | 161.0 | Rory Kinnear | 11000.0 | 200074175.0 | Action|Adventure|Thriller | ... | English | UK | PG-13 | 245000000.0 | 2015.0 | 393.0 | 6.8 | 2.35 | 85000 | 0.816629 |
3 | Color | Christopher Nolan | 813.0 | 164.0 | 22000.0 | 23000.0 | Christian Bale | 27000.0 | 448130642.0 | Action|Thriller | ... | English | USA | PG-13 | 250000000.0 | 2012.0 | 23000.0 | 8.5 | 2.35 | 164000 | 1.792523 |
4 | NaN | Doug Walker | NaN | NaN | 131.0 | NaN | Rob Walker | 131.0 | NaN | Documentary | ... | NaN | NaN | NaN | NaN | NaN | 12.0 | 7.1 | NaN | 0 | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
5038 | Color | Scott Smith | 1.0 | 87.0 | 2.0 | 318.0 | Daphne Zuniga | 637.0 | NaN | Comedy|Drama | ... | English | Canada | NaN | NaN | 2013.0 | 470.0 | 7.7 | NaN | 84 | NaN |
5039 | Color | NaN | 43.0 | 43.0 | NaN | 319.0 | Valorie Curry | 841.0 | NaN | Crime|Drama|Mystery|Thriller | ... | English | USA | TV-14 | NaN | NaN | 593.0 | 7.5 | 16.00 | 32000 | NaN |
5040 | Color | Benjamin Roberds | 13.0 | 76.0 | 0.0 | 0.0 | Maxwell Moody | 0.0 | NaN | Drama|Horror|Thriller | ... | English | USA | NaN | 1400.0 | 2013.0 | 0.0 | 6.3 | NaN | 16 | NaN |
5041 | Color | Daniel Hsia | 14.0 | 100.0 | 0.0 | 489.0 | Daniel Henney | 946.0 | 10443.0 | Comedy|Drama|Romance | ... | English | USA | PG-13 | NaN | 2012.0 | 719.0 | 6.3 | 2.35 | 660 | NaN |
5042 | Color | Jon Gunn | 43.0 | 90.0 | 16.0 | 16.0 | Brian Herzlinger | 86.0 | 85222.0 | Documentary | ... | English | USA | PG | 1100.0 | 2004.0 | 23.0 | 6.6 | 1.85 | 456 | 77.474545 |
5043 rows × 29 columns
(df
.assign(budget_delta=lambda x: x.gross / x.budget)
.sort_values('budget_delta', ascending=False))['movie_title']
4793 Paranormal Activity 4799 Tarnation 4707 The Blair Witch Project 4984 The Brothers McMullen 4936 The Texas Chain Saw Massacre ... 5036 The Mongol King 5038 Signed Sealed Delivered 5039 The Following 5040 A Plague So Pleasant 5041 Shanghai Calling Name: movie_title, Length: 5043, dtype: object