├── .gitignore ├── LICENSE ├── README.md ├── database └── README.md ├── pip_packages ├── README.md ├── numpy │ └── README.md ├── pandas │ ├── README.md │ └── netflix │ │ ├── netflix_titles.csv │ │ ├── pandas_example.ipynb │ │ ├── titles.csv │ │ └── titles.xlsx └── scipy │ └── README.md └── scripts ├── README.md ├── bitbucket ├── README.md └── main.py ├── data_vis ├── .gitignore ├── Procfile ├── main.py ├── netflix_titles.csv └── requirements.txt ├── omdbapi ├── README.md └── main.py └── web_scraper ├── README.md └── web_scraper.py /.gitignore: -------------------------------------------------------------------------------- 1 | **/.ipynb_checkpoints/ 2 | .DS_Store -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 mahbubzaman 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/lifeparticle/Python-Cheatsheet/issues) 2 | 3 | Table of Contents 4 | ================= 5 | 6 | * [Installation](#installation) 7 | * [How to install python](#how-to-install-python) 8 | * [Comment](#comment) 9 | * [Operators](#operators) 10 | * [Logical operators](#logical-operators) 11 | * [Bitwise operators](#bitwise-operators) 12 | * [Arithmetic operators](#arithmetic-operators) 13 | * [Comparison operators](#comparison-operators) 14 | * [Assignment operators](#assignment-operators) 15 | * [Conditional structures](#conditional-structures) 16 | * [Ternary Operators](#ternary-operators) 17 | * [Data types](#data-types) 18 | * [How to check data type](#how-to-check-data-type) 19 | * [String](#string) 20 | * [List](#list) 21 | * [Dictionary](#dictionary) 22 | * [Set](#set) 23 | * [map() Function](#map-function) 24 | * [filter() Function](#filter-function) 25 | * [reduce() Function](#reduce-function) 26 | * [zip() Function](#zip-function) 27 | * [format() function](#format-function) 28 | * [Miscellaneous](#miscellaneous) 29 | * [Get the number of bits in an integer in binary, excluding the sign and leading zeros.](#get-the-number-of-bits-in-an-integer-in-binary-excluding-the-sign-and-leading-zeros) 30 | * [Swap two variables in one line](#swap-two-variables-in-one-line) 31 | * [Books and other resources](#books-and-other-resources) 32 | * [Bug Reports and Feature Requests](#bug-reports-and-feature-requests) 33 | * [Author](#author) 34 | * [License](#license) 35 | 36 | Installation 37 | ============ 38 | 39 | How to install python 40 | ----- 41 | If you don't want to install python natively you can use [docker](https://www.docker.com/). 42 | ``` 43 | docker run -it --rm python:latest 44 | # check which version of python you're running 45 | python --version 46 | ``` 47 | 48 | 49 | Comment 50 | ============ 51 | 52 | ```python 53 | # single line comment 54 | 55 | # begin 56 | # multiline 57 | # comment 58 | # end 59 | ``` 60 | 61 | or 62 | ```python 63 | """ 64 | begin 65 | multiline 66 | comment 67 | end 68 | """ 69 | ``` 70 | 71 | Operators 72 | ============ 73 | Logical operators 74 | ----- 75 | | No | operator | 76 | |---|---| 77 | | 1 | and | 78 | | 2 | or | 79 | | 3 | not | 80 | | 4 | && | 81 | | 5 | \|\| | 82 | | 6 | ! | 83 | 84 | 85 | Bitwise operators 86 | ----- 87 | | No | operator | 88 | |---|---| 89 | | 1 | & | 90 | | 2 | \| | 91 | | 3 | ^ | 92 | | 4 | ~ | 93 | | 5 | << | 94 | | 6 | >> | 95 | 96 | Arithmetic operators 97 | ----- 98 | | No | operator | 99 | |---|---| 100 | | 1 | + | 101 | | 2 | - | 102 | | 3 | * | 103 | | 4 | / | 104 | | 5 | % | 105 | | 6 | ** | 106 | | 7 | // | 107 | 108 | Comparison operators 109 | ----- 110 | | No | operator | 111 | |---|---| 112 | | 1 | == | 113 | | 2 | != | 114 | | 3 | > | 115 | | 4 | < | 116 | | 5 | >= | 117 | | 6 | <= | 118 | | 7 | <> | 119 | 120 | 121 | Assignment operators 122 | ----- 123 | | No | operator | 124 | |---|---| 125 | | 1 | = | 126 | | 2 | += | 127 | | 3 | -= | 128 | | 4 | *= | 129 | | 5 | /= | 130 | | 6 | %= | 131 | | 7 | **= | 132 | | 8 | //= | 133 | 134 | 135 | Conditional structures 136 | ============ 137 | 138 | ```python 139 | x = 11 140 | if x > 10: 141 | print("The number is greater than 10") 142 | else: 143 | print("The number is not greater than 10") 144 | ``` 145 | 146 | ```python 147 | if x == 10: 148 | print('The value of x is 10') 149 | elif x == 11: 150 | print('The value of x is 11') 151 | else: 152 | print('The value of x is not either 10 or 11') 153 | ``` 154 | 155 | Ternary Operators 156 | ----- 157 | ``` 158 | execute_if_true if condition else execute_if_false 159 | ``` 160 | 161 | ```python 162 | print "even" if 6 % 2 == 0 else "odd" 163 | 164 | # output 165 | # even 166 | ``` 167 | 168 | Data types 169 | ============ 170 | 171 | | No | Type | Example | Class | Type | 172 | |---|---|---|---|---| 173 | | 1 | int | > a = 17 |> `a.__class__.__name__`
> 'int' | Numeric Types | 174 | | 2 | float | > a = 87.23 |> `a.__class__.__name__`
> 'float' | Numeric Types | 175 | | 3 | complex | > a = 1j or > a = 1J |> `a.__class__.__name__``
> 'complex' | Numeric Types | 176 | | 4 | str | > a = "Hello universe" |> `a.__class__.__name__`
> 'str' | Text Sequence Type | 177 | | 5 | list | > a = ["a", "b", "c"] |> `a.__class__.__name__`
> 'list' | Sequence Types | 178 | | 6 | tuple | > a = ("a", "b", "c") |> `a.__class__.__name__`
> 'tuple' | Sequence Types | 179 | | 7 | range | > a = range(7) |> `a.__class__.__name__`
> 'range' | Sequence Types | 180 | | 8 | dict | > a = {"name" : "Tom", "age" : 17} |> `a.__class__.__name__`
> 'dict' | Mapping Types | 181 | | 9 | set | > a = {"a", "b", "c"} |> `a.__class__.__name__`
> 'set' | Set Types | 182 | | 10 | frozenset | > a = frozenset({"a", "b", "c"}) |> `a.__class__.__name__`
> 'frozenset' | Set Types | 183 | | 11 | bool | > a = True |> `a.__class__.__name__`
> 'bool' | Boolean Types | 184 | | 12 | bytes | > a = b"Hello universe" |> `a.__class__.__name__`
> 'bytes' | Binary Sequence Types | 185 | | 13 | bytearray | > a = bytearray(7) |> `a.__class__.__name__`
> 'bytearray' | Binary Sequence Types | 186 | | 14 | memoryview | > a = memoryview(bytes(7)) |> `a.__class__.__name__`
> 'memoryview' | Binary Sequence Types | 187 | 188 | 189 | [Further readings](https://docs.python.org/3/library/stdtypes.html) 190 | 191 | 192 | How to check data type 193 | ----- 194 | 195 | ```python 196 | a = 37 197 | isinstance(a, int) 198 | # True 199 | isinstance(a, float) 200 | # False 201 | ``` 202 | 203 | ```python 204 | type(10) 205 | # 206 | ``` 207 | 208 | String 209 | ----- 210 | 211 | ```python 212 | print('Hello World'[0]) 213 | # H 214 | print('Hello World'[1]) 215 | # e 216 | ``` 217 | 218 | 219 | List 220 | ----- 221 | 222 | ```python 223 | a = ["a", "b", "c"] 224 | 225 | f, s, t = ["a", "b", "c"] 226 | # output 227 | # f = 'a' 228 | # s = 'b' 229 | # t = 'c' 230 | 231 | f, *mid, l = ["a", "b", "c"] 232 | 233 | # output 234 | # f = 'a' 235 | # mid = ['b'] 236 | # l = 'c' 237 | ``` 238 | 239 | ```python 240 | a = ["a", "b", "c"] 241 | b = ["d", "e", "f"] 242 | 243 | a.append(b) 244 | # output 245 | # ['a', 'b', 'c', ['d', 'e', 'f']] 246 | 247 | a = ["a", "b", "c"] 248 | b = ["d", "e", "f"] 249 | 250 | a.extend(b) 251 | # output 252 | # ['a', 'b', 'c', 'd', 'e', 'f'] 253 | ``` 254 | 255 | List slice 256 | 257 | list[start_index:stop_index:step] 258 | 259 | ```python 260 | numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 261 | 262 | numbers[0::2] 263 | 264 | # output 265 | # [1, 3, 5, 7, 9] 266 | 267 | 268 | numbers[1::2] 269 | 270 | # output 271 | # [2, 4, 6, 8, 10] 272 | ``` 273 | 274 | List Comprehensions 275 | 276 | Create a list of lists 277 | 278 | ```python 279 | [[0]*2 for i in range(2)] 280 | 281 | # output 282 | # [[0, 0], [0, 0]] 283 | ``` 284 | 285 | Create a list of variable length of lists 286 | 287 | ```python 288 | n = 10 289 | [[0]*(i+1) for i in range(n)] + [[0]*i for i in range(n-1, 0, -1)] 290 | 291 | # output 292 | # [[0], 293 | # [0, 0], 294 | # [0, 0, 0], 295 | # [0, 0, 0, 0], 296 | # [0, 0, 0, 0, 0], 297 | # [0, 0, 0, 0, 0, 0], 298 | # [0, 0, 0, 0, 0, 0, 0], 299 | # [0, 0, 0, 0, 0, 0, 0, 0], 300 | # [0, 0, 0, 0, 0, 0, 0, 0, 0], 301 | # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 302 | # [0, 0, 0, 0, 0, 0, 0, 0, 0], 303 | # [0, 0, 0, 0, 0, 0, 0, 0], 304 | # [0, 0, 0, 0, 0, 0, 0], 305 | # [0, 0, 0, 0, 0, 0], 306 | # [0, 0, 0, 0, 0], 307 | # [0, 0, 0, 0], 308 | # [0, 0, 0], 309 | # [0, 0], 310 | # [0]] 311 | ``` 312 | 313 | Create a matrix of multiplication tables 314 | 315 | ```python 316 | n = 5 317 | [[i*j for i in range(1,n+1)] for j in range(1, n+1)] 318 | 319 | # output 320 | # [[1, 2, 3, 4, 5], 321 | # [2, 4, 6, 8, 10], 322 | # [3, 6, 9, 12, 15], 323 | # [4, 8, 12, 16, 20], 324 | # [5, 10, 15, 20, 25]] 325 | ``` 326 | 327 | Dictionary 328 | ----- 329 | 330 | Dictionary Comprehensions 331 | 332 | ```python 333 | {c: ord(c) - 96 for c in string.ascii_lowercase[:26]} 334 | 335 | # output 336 | # {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4, 'g': 7, 'f': 6, 'i': 9, 'h': 8, 'k': 11, 'j': 10, 'm': 13, 'l': 12, 'o': 15, 'n': 14, 'q': 17, 'p': 16, 's': 19, 'r': 18, 'u': 21, 't': 20, 'w': 23, 'v': 22, 'y': 25, 'x': 24, 'z': 26} 337 | ``` 338 | 339 | Set 340 | ----- 341 | 342 | Set Comprehensions 343 | 344 | ```python 345 | numbers = [2, 2, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10, 11, 12, 13, 14] 346 | uniqueEvenNumbers = {num for num in numbers if num % 2 == 0} 347 | 348 | # output 349 | # set([2, 4, 6, 8, 10, 12, 14]) 350 | 351 | uniqueNumbers = set(numbers) 352 | 353 | # output 354 | # set([2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) 355 | ``` 356 | 357 | map() Function 358 | ============ 359 | 360 | ```python 361 | map(function, list) 362 | ``` 363 | 364 | ```python 365 | numbers = [1, 2, 3, 4, 5] 366 | squared = list(map(lambda n: n*n, numbers)) 367 | 368 | # output 369 | # [1, 4, 9, 16, 25] 370 | ``` 371 | 372 | filter() Function 373 | ============ 374 | 375 | ```python 376 | filter(function, list) 377 | ``` 378 | 379 | ```python 380 | numbers = [1, 2, 3, 4, 5] 381 | odd = list(filter(lambda n: n % 2 != 0, numbers)) 382 | 383 | # output 384 | # [1, 3, 5] 385 | ``` 386 | 387 | reduce() Function 388 | ============ 389 | 390 | ```python 391 | reduce(function, list) 392 | ``` 393 | 394 | ```python 395 | numbers = [1, 2, 3, 4, 5] 396 | sum = reduce(lambda x, y: x + y, numbers 397 | 398 | # output 399 | # 15 400 | ``` 401 | 402 | zip() Function 403 | ============ 404 | 405 | Creates an iterator of tuples. 406 | 407 | ```python 408 | zip(iterator1, iterator2, iterator3 ...) 409 | ``` 410 | 411 | ```python 412 | a = ['a', 'b', 'c'] 413 | b = [1, 2, 3] 414 | 415 | zip(a, b) 416 | 417 | # output 418 | # [('a', 1), ('b', 2), ('c', 3)] 419 | ``` 420 | 421 | tuples to list 422 | 423 | ```python 424 | list(map(list, zip(a, b))) 425 | 426 | # output 427 | # [['a', 1], ['b', 2], ['c', 3]] 428 | ``` 429 | 430 | format() function 431 | ============ 432 | 433 | ```python 434 | "{:{width}.{prec}f}".format(10.344, width=10, prec=6) 435 | 436 | # output 437 | # ' 10.344000' 438 | ``` 439 | 440 | 441 | Miscellaneous 442 | ============ 443 | 444 | Get the number of bits in an integer in binary, excluding the sign and leading zeros. 445 | ----- 446 | 447 | ```python 448 | n.bit_length() 449 | ``` 450 | 451 | Swap two variables in one line 452 | ----- 453 | 454 | ```python 455 | a, b = b, a 456 | ``` 457 | 458 | My Python Articles 459 | ============ 460 | 1. [How To Run a Python Script Using a Docker Container](https://towardsdatascience.com/how-to-run-a-python-script-using-a-docker-container-ea248e618e32) 461 | 2. [How To Create a Twitter Bot in Python](https://levelup.gitconnected.com/how-to-create-a-twitter-bot-in-python-bf49a384905f) 462 | 3. [How To Extract Text From Images Using Tesseract OCR Engine and Python](https://towardsdatascience.com/how-to-extract-text-from-images-using-tesseract-ocr-engine-and-python-22934125fdd5) 463 | 4. [How to Connect to a PostgreSQL Database With a Python Serverless Function](https://towardsdatascience.com/how-to-connect-to-a-postgresql-database-with-a-python-serverless-function-f5f3b244475) 464 | 5. [How to Deploy a Python Serverless Function to Vercel](https://towardsdatascience.com/how-to-deploy-a-python-serverless-function-to-vercel-f43c8ca393a0) 465 | 6. [How to Dockerize an Existing Flask Application](https://towardsdatascience.com/how-to-dockerize-an-existing-flask-application-115408463e1c) 466 | 7. [How To Create a Docker Image From a Container](https://python.plainenglish.io/how-to-create-a-docker-image-from-a-container-402f70f3dbf0) 467 | 468 | Books and other resources 469 | ============ 470 | 1. TODO 471 | 472 | Bug Reports and Feature Requests 473 | ============ 474 | Please create an issue with as much information you can. Thank you. 475 | 476 | Author 477 | ============ 478 | Mahbub Zaman (https://mahbub.ninja) 479 | 480 | License 481 | ============ 482 | MIT License 483 | -------------------------------------------------------------------------------- /database/README.md: -------------------------------------------------------------------------------- 1 | # todo 2 | -------------------------------------------------------------------------------- /pip_packages/README.md: -------------------------------------------------------------------------------- 1 | # Scientifics computing libraries 2 | 3 | 1. Pandas - Data structure 4 | 2. NumPy - Array 5 | 3. SciPy - Math 6 | 7 | 8 | # Data visualizations libraries 9 | 10 | 1. Matplotlib 11 | 2. Seaborn 12 | 13 | 14 | # Machine learning libraries 15 | 16 | 1. Scikit-learn 17 | 2. Statsmodels 18 | -------------------------------------------------------------------------------- /pip_packages/numpy/README.md: -------------------------------------------------------------------------------- 1 | ```python 2 | import numpy as np 3 | 4 | arr = np.arange(1,11) 5 | arr 6 | ``` 7 | 8 | ```python 9 | 10 | a = np.arange(15).reshape(3, 5) 11 | ``` 12 | 13 | 14 | ```python 15 | np.zeros((3, 3)) 16 | 17 | np.ones((3, 3)) 18 | ``` 19 | -------------------------------------------------------------------------------- /pip_packages/pandas/README.md: -------------------------------------------------------------------------------- 1 | # Data types 2 | 3 | ## Identify data types 4 | 5 | ```py 6 | df = pd.DataFrame({'id': ["1"]}) 7 | df.dtypes # name object 8 | ``` 9 | 10 | ## Convert data types 11 | 12 | ```py 13 | df['id'] = df['id'].astype('int') 14 | df.dtypes # id int64 15 | ``` 16 | 17 | | Python data type | Panda data type | Example | 18 | | ---------------- | --------------- |---------------| 19 | | float | float64 | 3.1416 | 20 | | int | int64 | 2, 3, 5 | 21 | | datetime | datetime64[ns] | '2007-07-13' | 22 | | string | object | 'A', 'E', 'I' | 23 | 24 | # Data formats 25 | 26 | | Data format | Read data | Write data | 27 | | ----------- | --------------- | ------------- | 28 | | csv | pd.read_csv() | pd.to_csv() | 29 | | json | pd.read_json() | pd.to_json() | 30 | | excel | pd.read_excel() | pd.to_excel() | 31 | | sql | pd.read_sql() | pd.to_sql() | 32 | 33 | 34 | # Data wrangling / data cleaning / data pre-processing 35 | 36 | ## Add values 37 | 38 | Add 1 to each entry of a column. 39 | 40 | ```py 41 | dataframe['column 1'] = dataframe['column 1'] + 1 42 | ``` 43 | 44 | ## Missing values 45 | 46 | Missing values could be represented as `N/A`, empty cell, etc. 47 | 48 | ### Drop missing values 49 | 50 | - Drop the variable 51 | - Drop the data entry 52 | 53 | ```py 54 | dataframe.dropna(subset=['column 1'], axis=0, inplace=True) # axis=0 drops the entire row 55 | dataframe.dropna(subset=['column 1'], axis=1, inplace=True) # axis=1 drops the entire column 56 | ``` 57 | 58 | ### Replace missing values 59 | 60 | - Use an average 61 | - Use frequency 62 | 63 | ```py 64 | mean = dataframe['column 1'].mean() 65 | dataframe['column 1'].replace(np.nan, mean) 66 | ``` 67 | 68 | ### Leave it as missing values 69 | 70 | ## Binning 71 | 72 | Grouping of values into **bins**. For example, We can categorize the price into low, medium and high price bins. 73 | 74 | ``` 75 | price: 10, 20, 30 40, 50, 60 70, 80. 90. 100 76 | bins: low mid high 77 | ``` 78 | 79 | ```py 80 | bins = np.linspace(min(dataframe['price']), max['price']), 4) 81 | bin_names = ['low', 'mid', 'high'] 82 | dataframe['price-binned'] = pd.cut(dataframe['price'], bins, labels=bin_names, include_lowest=True) 83 | ``` 84 | 85 | We can use data visulization like histograms to show the price distribution. 86 | 87 | 88 | # Converting categorical varaibles into quantitative varaibles 89 | 90 | A numerical variable used to identify categories is known as an indicator variable (or dummy variable). The reason they are referred to as "dummies" is since the numbers themselve have no meaning. To enable categorical variables for regression analysis in later modules, we use indicator variables. We can observe that the "fuel-type" column only has two distinct values: "gas" or "petrol." Since words are not understood by regression; only numbers are. We translate "fuel-type" into indicator variables in order to use this attribute in regression analysis. We will give numerical values to various fuel type categories using pandas' method `get_dummies`. 91 | 92 | 93 | ## One hot technique 94 | 95 | | Car | Fuel | gas | petrol | 96 | | ---------------- | --------------- |---------------|---------------| 97 | | A | gas | 1 | 0 | 98 | | B | petrol | 0 | 1 | 99 | | C | gas | 1 | 0 | 100 | 101 | ```py 102 | dummy_variable_2 = pd.get_dummies(dataframe['fuel']) 103 | dummy_variable_2.rename(columns={'gas':'fuel-gas', 'petrol': 'fuel-petrol'}, inplace=True) # change column names 104 | dataframe = pd.concat([dataframe, dummy_variable_2], axis=1) # merge new dataframe with old dataframe 105 | dataframe.drop('Fuel', axis = 1, inplace=True) # drop the fuel column 106 | ``` 107 | 108 | # Data formatting 109 | 110 | ``` 111 | Non-formatted -> Formatted 112 | SYD -> Sydney 113 | S.Y.D -> Sydney 114 | 115 | hard to compare -> easy to compare 116 | hard to aggregate -> easy to aggregate 117 | ``` 118 | 119 | # Data normalizations 120 | 121 | ## Feature scaling 122 | 123 | $$ 124 | x_{new} = x_{old}/x_{max} 125 | $$ 126 | 127 | ```py 128 | dataframe['column 1'] = dataframe['column 1']/dataframe['column 1'].max() 129 | ``` 130 | 131 | ## Min-Max 132 | 133 | $$ 134 | x_{new} = x_{old}-x_{min}/x_{max}-x_{min} 135 | $$ 136 | 137 | ```py 138 | dataframe['column 1'] = dataframe['column 1']-dataframe['column 1'].min()/dataframe['column 1'].max()-dataframe['column 1'].min() 139 | ``` 140 | 141 | ## Rename column name 142 | 143 | ```py 144 | dataframe.rename(columns={'column 1': 'column 2'}, inplace=True) 145 | ``` 146 | 147 | # Methods 148 | 149 | ## Show data types. 150 | 151 | ```py 152 | dataframe.dtypes 153 | ``` 154 | 155 | ## Show statistical information and skips rows and columns that do not contain numbers. 156 | 157 | ```py 158 | dataframe.describe() 159 | dataframe[['column 1', 'column 2']].describe() 160 | ``` 161 | 162 | ## Show statistical information for all columns. 163 | 164 | ```py 165 | dataframe.describe(include="all") 166 | ``` 167 | 168 | ## Show top and botton 30 rows. 169 | 170 | ```py 171 | dataframe.info() 172 | ``` 173 | 174 | ## Show first 5 rows 175 | 176 | ```py 177 | dataframe.head(5) 178 | ``` 179 | 180 | ## Show last 5 rows 181 | 182 | ```py 183 | dataframe.tail(5) 184 | ``` 185 | 186 | ## Create and set headers 187 | 188 | ```py 189 | headers = ["header1","header3","header3"] 190 | dataframe.columns = headers 191 | ``` 192 | 193 | ## Show name of the colmuns 194 | 195 | ```py 196 | dataframe.columns 197 | ``` 198 | 199 | # Resources 200 | 201 | 1. [Standardization vs Normalization Clearly Explained!](https://www.youtube.com/watch?v=sxEqtjLC0aM) 202 | -------------------------------------------------------------------------------- /pip_packages/pandas/netflix/pandas_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 164, 6 | "id": "f865f315", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import pandas as pd" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 165, 16 | "id": "2e5d4f4f", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# Load data from a CSV file\n", 21 | "df = pd.read_csv(\"netflix_titles.csv\")" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 166, 27 | "id": "08a77fb2", 28 | "metadata": {}, 29 | "outputs": [ 30 | { 31 | "data": { 32 | "text/plain": [ 33 | "(8807, 12)" 34 | ] 35 | }, 36 | "execution_count": 166, 37 | "metadata": {}, 38 | "output_type": "execute_result" 39 | } 40 | ], 41 | "source": [ 42 | "# Number of rows and columns\n", 43 | "df.shape" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 167, 49 | "id": "7d83cbcc", 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "name": "stdout", 54 | "output_type": "stream", 55 | "text": [ 56 | " show_id type title director \\\n", 57 | "0 s1 Movie Dick Johnson Is Dead Kirsten Johnson \n", 58 | "1 s2 TV Show Blood & Water NaN \n", 59 | "\n", 60 | " cast country \\\n", 61 | "0 NaN United States \n", 62 | "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa \n", 63 | "\n", 64 | " date_added release_year rating duration \\\n", 65 | "0 September 25, 2021 2020 PG-13 90 min \n", 66 | "1 September 24, 2021 2021 TV-MA 2 Seasons \n", 67 | "\n", 68 | " listed_in \\\n", 69 | "0 Documentaries \n", 70 | "1 International TV Shows, TV Dramas, TV Mysteries \n", 71 | "\n", 72 | " description \n", 73 | "0 As her father nears the end of his life, filmm... \n", 74 | "1 After crossing paths at a party, a Cape Town t... \n", 75 | " show_id type title director \\\n", 76 | "8805 s8806 Movie Zoom Peter Hewitt \n", 77 | "8806 s8807 Movie Zubaan Mozez Singh \n", 78 | "\n", 79 | " cast country \\\n", 80 | "8805 Tim Allen, Courteney Cox, Chevy Chase, Kate Ma... United States \n", 81 | "8806 Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan... India \n", 82 | "\n", 83 | " date_added release_year rating duration \\\n", 84 | "8805 January 11, 2020 2006 PG 88 min \n", 85 | "8806 March 2, 2019 2015 TV-14 111 min \n", 86 | "\n", 87 | " listed_in \\\n", 88 | "8805 Children & Family Movies, Comedies \n", 89 | "8806 Dramas, International Movies, Music & Musicals \n", 90 | "\n", 91 | " description \n", 92 | "8805 Dragged from civilian life, a former superhero... \n", 93 | "8806 A scrappy but poor boy worms his way into a ty... \n" 94 | ] 95 | } 96 | ], 97 | "source": [ 98 | "# show first 2 rows\n", 99 | "print(df.head(2))\n", 100 | "# show last 2 rows\n", 101 | "print(df.tail(2))" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 168, 107 | "id": "bec8100d", 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "name": "stdout", 112 | "output_type": "stream", 113 | "text": [ 114 | "Index(['show_id', 'type', 'title', 'director', 'cast', 'country', 'date_added',\n", 115 | " 'release_year', 'rating', 'duration', 'listed_in', 'description'],\n", 116 | " dtype='object')\n" 117 | ] 118 | } 119 | ], 120 | "source": [ 121 | "# show all the column names\n", 122 | "print(df.columns)" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 169, 128 | "id": "cd33f29f", 129 | "metadata": {}, 130 | "outputs": [ 131 | { 132 | "name": "stdout", 133 | "output_type": "stream", 134 | "text": [ 135 | "0 Dick Johnson Is Dead\n", 136 | "1 Blood & Water\n", 137 | "2 Ganglands\n", 138 | "3 Jailbirds New Orleans\n", 139 | "4 Kota Factory\n", 140 | "Name: title, dtype: object\n" 141 | ] 142 | } 143 | ], 144 | "source": [ 145 | "# show first 5 rows of a specific column\n", 146 | "print(df[\"title\"][0:5])" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 170, 152 | "id": "7d2d2f50", 153 | "metadata": {}, 154 | "outputs": [ 155 | { 156 | "name": "stdout", 157 | "output_type": "stream", 158 | "text": [ 159 | " title director\n", 160 | "0 Dick Johnson Is Dead Kirsten Johnson\n", 161 | "1 Blood & Water NaN\n", 162 | "2 Ganglands Julien Leclercq\n", 163 | "3 Jailbirds New Orleans NaN\n", 164 | "4 Kota Factory NaN\n" 165 | ] 166 | } 167 | ], 168 | "source": [ 169 | "# show first 5 rows of multiple columns\n", 170 | "print(df[[\"title\", \"director\"]][0:5])" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 171, 176 | "id": "aedd058f", 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "data": { 181 | "text/html": [ 182 | "
\n", 183 | "\n", 196 | "\n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
950s951MovieThe Lovely BonesPeter JacksonMark Wahlberg, Rachel Weisz, Susan Sarandon, S...United States, United Kingdom, New ZealandMay 1, 20212009PG-13136 minDramas, International Movies, Sci-Fi & FantasyIn the aftermath of her murder, 14-year-old Su...
8404s8405MovieThe Lord of the Rings: The Return of the KingPeter JacksonElijah Wood, Ian McKellen, Liv Tyler, Viggo Mo...New Zealand, United StatesJanuary 1, 20202003PG-13201 minAction & Adventure, Sci-Fi & FantasyAragorn is revealed as the heir to the ancient...
8405s8406MovieThe Lord of the Rings: The Two TowersPeter JacksonElijah Wood, Ian McKellen, Liv Tyler, Viggo Mo...New Zealand, United StatesJanuary 1, 20202002PG-13179 minAction & Adventure, Sci-Fi & FantasyFrodo and Sam head to Mordor to destroy the On...
\n", 262 | "
" 263 | ], 264 | "text/plain": [ 265 | " show_id type title \\\n", 266 | "950 s951 Movie The Lovely Bones \n", 267 | "8404 s8405 Movie The Lord of the Rings: The Return of the King \n", 268 | "8405 s8406 Movie The Lord of the Rings: The Two Towers \n", 269 | "\n", 270 | " director cast \\\n", 271 | "950 Peter Jackson Mark Wahlberg, Rachel Weisz, Susan Sarandon, S... \n", 272 | "8404 Peter Jackson Elijah Wood, Ian McKellen, Liv Tyler, Viggo Mo... \n", 273 | "8405 Peter Jackson Elijah Wood, Ian McKellen, Liv Tyler, Viggo Mo... \n", 274 | "\n", 275 | " country date_added \\\n", 276 | "950 United States, United Kingdom, New Zealand May 1, 2021 \n", 277 | "8404 New Zealand, United States January 1, 2020 \n", 278 | "8405 New Zealand, United States January 1, 2020 \n", 279 | "\n", 280 | " release_year rating duration \\\n", 281 | "950 2009 PG-13 136 min \n", 282 | "8404 2003 PG-13 201 min \n", 283 | "8405 2002 PG-13 179 min \n", 284 | "\n", 285 | " listed_in \\\n", 286 | "950 Dramas, International Movies, Sci-Fi & Fantasy \n", 287 | "8404 Action & Adventure, Sci-Fi & Fantasy \n", 288 | "8405 Action & Adventure, Sci-Fi & Fantasy \n", 289 | "\n", 290 | " description \n", 291 | "950 In the aftermath of her murder, 14-year-old Su... \n", 292 | "8404 Aragorn is revealed as the heir to the ancient... \n", 293 | "8405 Frodo and Sam head to Mordor to destroy the On... " 294 | ] 295 | }, 296 | "execution_count": 171, 297 | "metadata": {}, 298 | "output_type": "execute_result" 299 | } 300 | ], 301 | "source": [ 302 | "# select rows based on a condition\n", 303 | "df.loc[df[\"director\"] == \"Peter Jackson\"]" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": 172, 309 | "id": "b0dfa526", 310 | "metadata": {}, 311 | "outputs": [ 312 | { 313 | "data": { 314 | "text/html": [ 315 | "
\n", 316 | "\n", 329 | "\n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | "
release_year
count8807.000000
mean2014.180198
std8.819312
min1925.000000
25%2013.000000
50%2017.000000
75%2019.000000
max2021.000000
\n", 371 | "
" 372 | ], 373 | "text/plain": [ 374 | " release_year\n", 375 | "count 8807.000000\n", 376 | "mean 2014.180198\n", 377 | "std 8.819312\n", 378 | "min 1925.000000\n", 379 | "25% 2013.000000\n", 380 | "50% 2017.000000\n", 381 | "75% 2019.000000\n", 382 | "max 2021.000000" 383 | ] 384 | }, 385 | "execution_count": 172, 386 | "metadata": {}, 387 | "output_type": "execute_result" 388 | } 389 | ], 390 | "source": [ 391 | "df.describe()" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 173, 397 | "id": "805895fb", 398 | "metadata": {}, 399 | "outputs": [ 400 | { 401 | "data": { 402 | "text/html": [ 403 | "
\n", 404 | "\n", 417 | "\n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
4250s4251TV ShowPioneers: First Women Filmmakers*NaNNaNNaNDecember 30, 20181925TV-141 SeasonTV ShowsThis collection restores films from women who ...
7790s7791MoviePrelude to WarFrank CapraNaNUnited StatesMarch 31, 20171942TV-1452 minClassic Movies, DocumentariesFrank Capra's documentary chronicles the rise ...
8205s8206MovieThe Battle of MidwayJohn FordHenry Fonda, Jane DarwellUnited StatesMarch 31, 20171942TV-1418 minClassic Movies, DocumentariesDirector John Ford captures combat footage of ...
8660s8661MovieUndercover: How to Operate Behind Enemy LinesJohn FordNaNUnited StatesMarch 31, 20171943TV-PG61 minClassic Movies, DocumentariesThis World War II-era training film dramatizes...
8739s8740MovieWhy We Fight: The Battle of RussiaFrank Capra, Anatole LitvakNaNUnited StatesMarch 31, 20171943TV-PG82 minDocumentariesThis installment of Frank Capra's acclaimed do...
\n", 513 | "
" 514 | ], 515 | "text/plain": [ 516 | " show_id type title \\\n", 517 | "4250 s4251 TV Show Pioneers: First Women Filmmakers* \n", 518 | "7790 s7791 Movie Prelude to War \n", 519 | "8205 s8206 Movie The Battle of Midway \n", 520 | "8660 s8661 Movie Undercover: How to Operate Behind Enemy Lines \n", 521 | "8739 s8740 Movie Why We Fight: The Battle of Russia \n", 522 | "\n", 523 | " director cast country \\\n", 524 | "4250 NaN NaN NaN \n", 525 | "7790 Frank Capra NaN United States \n", 526 | "8205 John Ford Henry Fonda, Jane Darwell United States \n", 527 | "8660 John Ford NaN United States \n", 528 | "8739 Frank Capra, Anatole Litvak NaN United States \n", 529 | "\n", 530 | " date_added release_year rating duration \\\n", 531 | "4250 December 30, 2018 1925 TV-14 1 Season \n", 532 | "7790 March 31, 2017 1942 TV-14 52 min \n", 533 | "8205 March 31, 2017 1942 TV-14 18 min \n", 534 | "8660 March 31, 2017 1943 TV-PG 61 min \n", 535 | "8739 March 31, 2017 1943 TV-PG 82 min \n", 536 | "\n", 537 | " listed_in \\\n", 538 | "4250 TV Shows \n", 539 | "7790 Classic Movies, Documentaries \n", 540 | "8205 Classic Movies, Documentaries \n", 541 | "8660 Classic Movies, Documentaries \n", 542 | "8739 Documentaries \n", 543 | "\n", 544 | " description \n", 545 | "4250 This collection restores films from women who ... \n", 546 | "7790 Frank Capra's documentary chronicles the rise ... \n", 547 | "8205 Director John Ford captures combat footage of ... \n", 548 | "8660 This World War II-era training film dramatizes... \n", 549 | "8739 This installment of Frank Capra's acclaimed do... " 550 | ] 551 | }, 552 | "execution_count": 173, 553 | "metadata": {}, 554 | "output_type": "execute_result" 555 | } 556 | ], 557 | "source": [ 558 | "# ascending order, smallest to largest\n", 559 | "df.sort_values(\"release_year\")[0:5]" 560 | ] 561 | }, 562 | { 563 | "cell_type": "code", 564 | "execution_count": 174, 565 | "id": "578318b3", 566 | "metadata": {}, 567 | "outputs": [ 568 | { 569 | "data": { 570 | "text/html": [ 571 | "
\n", 572 | "\n", 585 | "\n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
693s694MovieAli & Ratu Ratu QueensLucky KuswandiIqbaal Ramadhan, Nirina Zubir, Asri Welas, Tik...NaNJune 17, 20212021TV-14101 minComedies, Dramas, International MoviesAfter his father's passing, a teenager sets ou...
781s782MovieBlack Holes | The Edge of All We KnowPeter GalisonNaNNaNJune 2, 20212021TV-1499 minDocumentariesFollow scientists on their quest to understand...
762s763MovieSweet & SourLee Kae-byeokJang Ki-yong, Chae Soo-bin, Jung Soo-jungSouth KoreaJune 4, 20212021TV-14103 minComedies, International Movies, Romantic MoviesFaced with real-world opportunities and challe...
763s764TV ShowSweet ToothNaNNonso Anozie, Christian Convery, Adeel Akhtar,...United StatesJune 4, 20212021TV-141 SeasonTV Action & Adventure, TV Dramas, TV Sci-Fi & ...On a perilous adventure across a post-apocalyp...
764s765MovieTrippin' with the KandasamysJayan MoodleyJailoshini Naidoo, Maeshni Naicker, Madhushan ...South AfricaJune 4, 20212021TV-1494 minComedies, International Movies, Romantic MoviesTo rekindle their marriages, best friends-turn...
\n", 681 | "
" 682 | ], 683 | "text/plain": [ 684 | " show_id type title director \\\n", 685 | "693 s694 Movie Ali & Ratu Ratu Queens Lucky Kuswandi \n", 686 | "781 s782 Movie Black Holes | The Edge of All We Know Peter Galison \n", 687 | "762 s763 Movie Sweet & Sour Lee Kae-byeok \n", 688 | "763 s764 TV Show Sweet Tooth NaN \n", 689 | "764 s765 Movie Trippin' with the Kandasamys Jayan Moodley \n", 690 | "\n", 691 | " cast country \\\n", 692 | "693 Iqbaal Ramadhan, Nirina Zubir, Asri Welas, Tik... NaN \n", 693 | "781 NaN NaN \n", 694 | "762 Jang Ki-yong, Chae Soo-bin, Jung Soo-jung South Korea \n", 695 | "763 Nonso Anozie, Christian Convery, Adeel Akhtar,... United States \n", 696 | "764 Jailoshini Naidoo, Maeshni Naicker, Madhushan ... South Africa \n", 697 | "\n", 698 | " date_added release_year rating duration \\\n", 699 | "693 June 17, 2021 2021 TV-14 101 min \n", 700 | "781 June 2, 2021 2021 TV-14 99 min \n", 701 | "762 June 4, 2021 2021 TV-14 103 min \n", 702 | "763 June 4, 2021 2021 TV-14 1 Season \n", 703 | "764 June 4, 2021 2021 TV-14 94 min \n", 704 | "\n", 705 | " listed_in \\\n", 706 | "693 Comedies, Dramas, International Movies \n", 707 | "781 Documentaries \n", 708 | "762 Comedies, International Movies, Romantic Movies \n", 709 | "763 TV Action & Adventure, TV Dramas, TV Sci-Fi & ... \n", 710 | "764 Comedies, International Movies, Romantic Movies \n", 711 | "\n", 712 | " description \n", 713 | "693 After his father's passing, a teenager sets ou... \n", 714 | "781 Follow scientists on their quest to understand... \n", 715 | "762 Faced with real-world opportunities and challe... \n", 716 | "763 On a perilous adventure across a post-apocalyp... \n", 717 | "764 To rekindle their marriages, best friends-turn... " 718 | ] 719 | }, 720 | "execution_count": 174, 721 | "metadata": {}, 722 | "output_type": "execute_result" 723 | } 724 | ], 725 | "source": [ 726 | "# decending order, largest to smallest\n", 727 | "df.sort_values(\"release_year\", ascending=False)[0:5]" 728 | ] 729 | }, 730 | { 731 | "cell_type": "code", 732 | "execution_count": 175, 733 | "id": "83ea2cd4", 734 | "metadata": {}, 735 | "outputs": [ 736 | { 737 | "data": { 738 | "text/html": [ 739 | "
\n", 740 | "\n", 753 | "\n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | " \n", 790 | " \n", 791 | " \n", 792 | " \n", 793 | " \n", 794 | " \n", 795 | " \n", 796 | " \n", 797 | " \n", 798 | " \n", 799 | " \n", 800 | " \n", 801 | " \n", 802 | " \n", 803 | " \n", 804 | " \n", 805 | " \n", 806 | " \n", 807 | " \n", 808 | " \n", 809 | " \n", 810 | " \n", 811 | " \n", 812 | " \n", 813 | " \n", 814 | " \n", 815 | " \n", 816 | " \n", 817 | " \n", 818 | " \n", 819 | " \n", 820 | " \n", 821 | " \n", 822 | " \n", 823 | " \n", 824 | " \n", 825 | " \n", 826 | " \n", 827 | " \n", 828 | " \n", 829 | " \n", 830 | " \n", 831 | " \n", 832 | " \n", 833 | " \n", 834 | " \n", 835 | " \n", 836 | " \n", 837 | " \n", 838 | " \n", 839 | " \n", 840 | " \n", 841 | " \n", 842 | " \n", 843 | " \n", 844 | " \n", 845 | " \n", 846 | " \n", 847 | " \n", 848 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
88s89MovieBlood Brothers: Malcolm X & Muhammad AliMarcus ClarkeMalcolm X, Muhammad AliNaNSeptember 9, 20212021PG-1396 minDocumentaries, Sports MoviesFrom a chance meeting to a tragic fallout, Mal...
91s92MovieThe Women and the MurdererMona Achache, Patricia TourancheauNaNFranceSeptember 9, 20212021TV-1492 minDocumentaries, International MoviesThis documentary traces the capture of serial ...
92s93TV ShowInto the NightNaNPauline Etienne, Laurent Capelluto, Stefano Ca...BelgiumSeptember 8, 20212021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesPassengers and crew aboard a hijacked overnigh...
93s94MovieJJ+EAlexis AlmströmElsa Öhrn, Mustapha Aarab, Jonay Pineda Skalla...NaNSeptember 8, 20212021TV-MA91 minDramas, International Movies, Romantic MoviesElisabeth and John-John live in the same city,...
95s96TV ShowThe CircleNaNMichelle ButeauUnited States, United KingdomSeptember 8, 20212021TV-MA3 SeasonsReality TVStatus and strategy collide in this social exp...
\n", 849 | "
" 850 | ], 851 | "text/plain": [ 852 | " show_id type title \\\n", 853 | "88 s89 Movie Blood Brothers: Malcolm X & Muhammad Ali \n", 854 | "91 s92 Movie The Women and the Murderer \n", 855 | "92 s93 TV Show Into the Night \n", 856 | "93 s94 Movie JJ+E \n", 857 | "95 s96 TV Show The Circle \n", 858 | "\n", 859 | " director \\\n", 860 | "88 Marcus Clarke \n", 861 | "91 Mona Achache, Patricia Tourancheau \n", 862 | "92 NaN \n", 863 | "93 Alexis Almström \n", 864 | "95 NaN \n", 865 | "\n", 866 | " cast \\\n", 867 | "88 Malcolm X, Muhammad Ali \n", 868 | "91 NaN \n", 869 | "92 Pauline Etienne, Laurent Capelluto, Stefano Ca... \n", 870 | "93 Elsa Öhrn, Mustapha Aarab, Jonay Pineda Skalla... \n", 871 | "95 Michelle Buteau \n", 872 | "\n", 873 | " country date_added release_year rating \\\n", 874 | "88 NaN September 9, 2021 2021 PG-13 \n", 875 | "91 France September 9, 2021 2021 TV-14 \n", 876 | "92 Belgium September 8, 2021 2021 TV-MA \n", 877 | "93 NaN September 8, 2021 2021 TV-MA \n", 878 | "95 United States, United Kingdom September 8, 2021 2021 TV-MA \n", 879 | "\n", 880 | " duration listed_in \\\n", 881 | "88 96 min Documentaries, Sports Movies \n", 882 | "91 92 min Documentaries, International Movies \n", 883 | "92 2 Seasons International TV Shows, TV Dramas, TV Mysteries \n", 884 | "93 91 min Dramas, International Movies, Romantic Movies \n", 885 | "95 3 Seasons Reality TV \n", 886 | "\n", 887 | " description \n", 888 | "88 From a chance meeting to a tragic fallout, Mal... \n", 889 | "91 This documentary traces the capture of serial ... \n", 890 | "92 Passengers and crew aboard a hijacked overnigh... \n", 891 | "93 Elisabeth and John-John live in the same city,... \n", 892 | "95 Status and strategy collide in this social exp... " 893 | ] 894 | }, 895 | "execution_count": 175, 896 | "metadata": {}, 897 | "output_type": "execute_result" 898 | } 899 | ], 900 | "source": [ 901 | "# decending order, multiple columns\n", 902 | "df.sort_values([\"release_year\", \"date_added\"], ascending=False)[0:5]" 903 | ] 904 | }, 905 | { 906 | "cell_type": "code", 907 | "execution_count": 176, 908 | "id": "15772fb1", 909 | "metadata": {}, 910 | "outputs": [ 911 | { 912 | "data": { 913 | "text/html": [ 914 | "
\n", 915 | "\n", 928 | "\n", 929 | " \n", 930 | " \n", 931 | " \n", 932 | " \n", 933 | " \n", 934 | " \n", 935 | " \n", 936 | " \n", 937 | " \n", 938 | " \n", 939 | " \n", 940 | " \n", 941 | " \n", 942 | " \n", 943 | " \n", 944 | " \n", 945 | " \n", 946 | " \n", 947 | " \n", 948 | " \n", 949 | " \n", 950 | " \n", 951 | " \n", 952 | " \n", 953 | " \n", 954 | " \n", 955 | " \n", 956 | " \n", 957 | " \n", 958 | " \n", 959 | " \n", 960 | " \n", 961 | " \n", 962 | " \n", 963 | " \n", 964 | " \n", 965 | " \n", 966 | " \n", 967 | " \n", 968 | " \n", 969 | " \n", 970 | " \n", 971 | " \n", 972 | " \n", 973 | " \n", 974 | " \n", 975 | "
typetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
0MovieDick Johnson Is DeadKirsten JohnsonNaNUnited StatesSeptember 25, 20212020PG-1390 minDocumentariesAs her father nears the end of his life, filmm...
1TV ShowBlood & WaterNaNAma Qamata, Khosi Ngema, Gail Mabalane, Thaban...South AfricaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesAfter crossing paths at a party, a Cape Town t...
\n", 976 | "
" 977 | ], 978 | "text/plain": [ 979 | " type title director \\\n", 980 | "0 Movie Dick Johnson Is Dead Kirsten Johnson \n", 981 | "1 TV Show Blood & Water NaN \n", 982 | "\n", 983 | " cast country \\\n", 984 | "0 NaN United States \n", 985 | "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa \n", 986 | "\n", 987 | " date_added release_year rating duration \\\n", 988 | "0 September 25, 2021 2020 PG-13 90 min \n", 989 | "1 September 24, 2021 2021 TV-MA 2 Seasons \n", 990 | "\n", 991 | " listed_in \\\n", 992 | "0 Documentaries \n", 993 | "1 International TV Shows, TV Dramas, TV Mysteries \n", 994 | "\n", 995 | " description \n", 996 | "0 As her father nears the end of his life, filmm... \n", 997 | "1 After crossing paths at a party, a Cape Town t... " 998 | ] 999 | }, 1000 | "execution_count": 176, 1001 | "metadata": {}, 1002 | "output_type": "execute_result" 1003 | } 1004 | ], 1005 | "source": [ 1006 | "# drop a column\n", 1007 | "df_no_id = df.drop(columns=[\"show_id\"])\n", 1008 | "df_no_id.head(2)" 1009 | ] 1010 | }, 1011 | { 1012 | "cell_type": "code", 1013 | "execution_count": 177, 1014 | "id": "947e892d", 1015 | "metadata": {}, 1016 | "outputs": [], 1017 | "source": [ 1018 | "# export data\n", 1019 | "df_titles = df[\"title\"]\n", 1020 | "df_titles.to_csv(\"titles.csv\")\n", 1021 | "df_titles.to_excel(\"titles.xlsx\")" 1022 | ] 1023 | }, 1024 | { 1025 | "cell_type": "code", 1026 | "execution_count": 178, 1027 | "id": "408a5d07", 1028 | "metadata": {}, 1029 | "outputs": [ 1030 | { 1031 | "data": { 1032 | "text/html": [ 1033 | "
\n", 1034 | "\n", 1047 | "\n", 1048 | " \n", 1049 | " \n", 1050 | " \n", 1051 | " \n", 1052 | " \n", 1053 | " \n", 1054 | " \n", 1055 | " \n", 1056 | " \n", 1057 | " \n", 1058 | " \n", 1059 | " \n", 1060 | " \n", 1061 | " \n", 1062 | " \n", 1063 | " \n", 1064 | " \n", 1065 | " \n", 1066 | " \n", 1067 | " \n", 1068 | " \n", 1069 | " \n", 1070 | " \n", 1071 | " \n", 1072 | " \n", 1073 | " \n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
0s1MovieDick Johnson Is DeadKirsten JohnsonNaNUnited StatesSeptember 25, 20212020PG-1390 minDocumentariesAs her father nears the end of his life, filmm...
1s2TV ShowBlood & WaterNaNAma Qamata, Khosi Ngema, Gail Mabalane, Thaban...South AfricaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesAfter crossing paths at a party, a Cape Town t...
\n", 1098 | "
" 1099 | ], 1100 | "text/plain": [ 1101 | " show_id type title director \\\n", 1102 | "0 s1 Movie Dick Johnson Is Dead Kirsten Johnson \n", 1103 | "1 s2 TV Show Blood & Water NaN \n", 1104 | "\n", 1105 | " cast country \\\n", 1106 | "0 NaN United States \n", 1107 | "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa \n", 1108 | "\n", 1109 | " date_added release_year rating duration \\\n", 1110 | "0 September 25, 2021 2020 PG-13 90 min \n", 1111 | "1 September 24, 2021 2021 TV-MA 2 Seasons \n", 1112 | "\n", 1113 | " listed_in \\\n", 1114 | "0 Documentaries \n", 1115 | "1 International TV Shows, TV Dramas, TV Mysteries \n", 1116 | "\n", 1117 | " description \n", 1118 | "0 As her father nears the end of his life, filmm... \n", 1119 | "1 After crossing paths at a party, a Cape Town t... " 1120 | ] 1121 | }, 1122 | "execution_count": 178, 1123 | "metadata": {}, 1124 | "output_type": "execute_result" 1125 | } 1126 | ], 1127 | "source": [ 1128 | "df.head(2)" 1129 | ] 1130 | }, 1131 | { 1132 | "cell_type": "code", 1133 | "execution_count": 179, 1134 | "id": "1b683ebf", 1135 | "metadata": {}, 1136 | "outputs": [ 1137 | { 1138 | "data": { 1139 | "text/html": [ 1140 | "
\n", 1141 | "\n", 1154 | "\n", 1155 | " \n", 1156 | " \n", 1157 | " \n", 1158 | " \n", 1159 | " \n", 1160 | " \n", 1161 | " \n", 1162 | " \n", 1163 | " \n", 1164 | " \n", 1165 | " \n", 1166 | " \n", 1167 | " \n", 1168 | " \n", 1169 | " \n", 1170 | " \n", 1171 | " \n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | " \n", 1176 | " \n", 1177 | " \n", 1178 | " \n", 1179 | " \n", 1180 | " \n", 1181 | " \n", 1182 | " \n", 1183 | " \n", 1184 | " \n", 1185 | " \n", 1186 | " \n", 1187 | " \n", 1188 | " \n", 1189 | " \n", 1190 | " \n", 1191 | " \n", 1192 | " \n", 1193 | " \n", 1194 | " \n", 1195 | " \n", 1196 | " \n", 1197 | " \n", 1198 | " \n", 1199 | " \n", 1200 | " \n", 1201 | " \n", 1202 | " \n", 1203 | " \n", 1204 | " \n", 1205 | " \n", 1206 | " \n", 1207 | " \n", 1208 | " \n", 1209 | " \n", 1210 | " \n", 1211 | " \n", 1212 | " \n", 1213 | " \n", 1214 | " \n", 1215 | " \n", 1216 | " \n", 1217 | " \n", 1218 | " \n", 1219 | " \n", 1220 | " \n", 1221 | " \n", 1222 | " \n", 1223 | " \n", 1224 | " \n", 1225 | " \n", 1226 | " \n", 1227 | " \n", 1228 | " \n", 1229 | " \n", 1230 | " \n", 1231 | " \n", 1232 | " \n", 1233 | " \n", 1234 | " \n", 1235 | " \n", 1236 | " \n", 1237 | " \n", 1238 | " \n", 1239 | " \n", 1240 | " \n", 1241 | " \n", 1242 | " \n", 1243 | " \n", 1244 | " \n", 1245 | " \n", 1246 | " \n", 1247 | " \n", 1248 | " \n", 1249 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
0s1MovieDick Johnson Is DeadKirsten JohnsonNaNUnited StatesSeptember 25, 20212020PG-1390 minDocumentariesAs her father nears the end of his life, filmm...
6s7MovieMy Little Pony: A New GenerationRobert Cullen, José Luis UchaVanessa Hudgens, Kimiko Glenn, James Marsden, ...NaNSeptember 24, 20212021PG91 minChildren & Family MoviesEquestria's divided. But a bright-eyed hero be...
7s8MovieSankofaHaile GerimaKofi Ghanaba, Oyafunmike Ogunlano, Alexandra D...United States, Ghana, Burkina Faso, United Kin...September 24, 20211993TV-MA125 minDramas, Independent Movies, International MoviesOn a photo shoot in Ghana, an American model s...
9s10MovieThe StarlingTheodore MelfiMelissa McCarthy, Chris O'Dowd, Kevin Kline, T...United StatesSeptember 24, 20212021PG-13104 minComedies, DramasA woman adjusting to life after a loss contend...
12s13MovieJe Suis KarlChristian SchwochowLuna Wedler, Jannis Niewöhner, Milan Peschel, ...Germany, Czech RepublicSeptember 23, 20212021TV-MA127 minDramas, International MoviesAfter most of her family is murdered in a terr...
\n", 1250 | "
" 1251 | ], 1252 | "text/plain": [ 1253 | " show_id type title \\\n", 1254 | "0 s1 Movie Dick Johnson Is Dead \n", 1255 | "6 s7 Movie My Little Pony: A New Generation \n", 1256 | "7 s8 Movie Sankofa \n", 1257 | "9 s10 Movie The Starling \n", 1258 | "12 s13 Movie Je Suis Karl \n", 1259 | "\n", 1260 | " director \\\n", 1261 | "0 Kirsten Johnson \n", 1262 | "6 Robert Cullen, José Luis Ucha \n", 1263 | "7 Haile Gerima \n", 1264 | "9 Theodore Melfi \n", 1265 | "12 Christian Schwochow \n", 1266 | "\n", 1267 | " cast \\\n", 1268 | "0 NaN \n", 1269 | "6 Vanessa Hudgens, Kimiko Glenn, James Marsden, ... \n", 1270 | "7 Kofi Ghanaba, Oyafunmike Ogunlano, Alexandra D... \n", 1271 | "9 Melissa McCarthy, Chris O'Dowd, Kevin Kline, T... \n", 1272 | "12 Luna Wedler, Jannis Niewöhner, Milan Peschel, ... \n", 1273 | "\n", 1274 | " country date_added \\\n", 1275 | "0 United States September 25, 2021 \n", 1276 | "6 NaN September 24, 2021 \n", 1277 | "7 United States, Ghana, Burkina Faso, United Kin... September 24, 2021 \n", 1278 | "9 United States September 24, 2021 \n", 1279 | "12 Germany, Czech Republic September 23, 2021 \n", 1280 | "\n", 1281 | " release_year rating duration \\\n", 1282 | "0 2020 PG-13 90 min \n", 1283 | "6 2021 PG 91 min \n", 1284 | "7 1993 TV-MA 125 min \n", 1285 | "9 2021 PG-13 104 min \n", 1286 | "12 2021 TV-MA 127 min \n", 1287 | "\n", 1288 | " listed_in \\\n", 1289 | "0 Documentaries \n", 1290 | "6 Children & Family Movies \n", 1291 | "7 Dramas, Independent Movies, International Movies \n", 1292 | "9 Comedies, Dramas \n", 1293 | "12 Dramas, International Movies \n", 1294 | "\n", 1295 | " description \n", 1296 | "0 As her father nears the end of his life, filmm... \n", 1297 | "6 Equestria's divided. But a bright-eyed hero be... \n", 1298 | "7 On a photo shoot in Ghana, an American model s... \n", 1299 | "9 A woman adjusting to life after a loss contend... \n", 1300 | "12 After most of her family is murdered in a terr... " 1301 | ] 1302 | }, 1303 | "execution_count": 179, 1304 | "metadata": {}, 1305 | "output_type": "execute_result" 1306 | } 1307 | ], 1308 | "source": [ 1309 | "# Filter\n", 1310 | "df_movies = df[df['type'] =='Movie']\n", 1311 | "df_movies.head(5)" 1312 | ] 1313 | }, 1314 | { 1315 | "cell_type": "code", 1316 | "execution_count": 180, 1317 | "id": "31cd5782", 1318 | "metadata": { 1319 | "scrolled": true 1320 | }, 1321 | "outputs": [ 1322 | { 1323 | "data": { 1324 | "text/html": [ 1325 | "
\n", 1326 | "\n", 1339 | "\n", 1340 | " \n", 1341 | " \n", 1342 | " \n", 1343 | " \n", 1344 | " \n", 1345 | " \n", 1346 | " \n", 1347 | " \n", 1348 | " \n", 1349 | " \n", 1350 | " \n", 1351 | " \n", 1352 | " \n", 1353 | " \n", 1354 | " \n", 1355 | " \n", 1356 | " \n", 1357 | " \n", 1358 | " \n", 1359 | " \n", 1360 | " \n", 1361 | " \n", 1362 | " \n", 1363 | " \n", 1364 | " \n", 1365 | " \n", 1366 | " \n", 1367 | " \n", 1368 | " \n", 1369 | " \n", 1370 | " \n", 1371 | " \n", 1372 | " \n", 1373 | " \n", 1374 | " \n", 1375 | " \n", 1376 | " \n", 1377 | " \n", 1378 | " \n", 1379 | " \n", 1380 | " \n", 1381 | " \n", 1382 | " \n", 1383 | " \n", 1384 | " \n", 1385 | " \n", 1386 | " \n", 1387 | " \n", 1388 | " \n", 1389 | " \n", 1390 | " \n", 1391 | " \n", 1392 | " \n", 1393 | " \n", 1394 | " \n", 1395 | " \n", 1396 | " \n", 1397 | " \n", 1398 | " \n", 1399 | " \n", 1400 | " \n", 1401 | " \n", 1402 | " \n", 1403 | " \n", 1404 | " \n", 1405 | " \n", 1406 | " \n", 1407 | " \n", 1408 | " \n", 1409 | " \n", 1410 | " \n", 1411 | " \n", 1412 | " \n", 1413 | " \n", 1414 | " \n", 1415 | " \n", 1416 | " \n", 1417 | " \n", 1418 | " \n", 1419 | " \n", 1420 | " \n", 1421 | " \n", 1422 | " \n", 1423 | " \n", 1424 | " \n", 1425 | " \n", 1426 | " \n", 1427 | " \n", 1428 | " \n", 1429 | " \n", 1430 | " \n", 1431 | " \n", 1432 | " \n", 1433 | " \n", 1434 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
1s2TV ShowBlood & WaterNaNAma Qamata, Khosi Ngema, Gail Mabalane, Thaban...South AfricaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesAfter crossing paths at a party, a Cape Town t...
2s3TV ShowGanglandsJulien LeclercqSami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...NaNSeptember 24, 20212021TV-MA1 SeasonCrime TV Shows, International TV Shows, TV Act...To protect his family from a powerful drug lor...
3s4TV ShowJailbirds New OrleansNaNNaNNaNSeptember 24, 20212021TV-MA1 SeasonDocuseries, Reality TVFeuds, flirtations and toilet talk go down amo...
4s5TV ShowKota FactoryNaNMayur More, Jitendra Kumar, Ranjan Raj, Alam K...IndiaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, Romantic TV Shows, TV ...In a city of coaching centers known to train I...
5s6TV ShowMidnight MassMike FlanaganKate Siegel, Zach Gilford, Hamish Linklater, H...NaNSeptember 24, 20212021TV-MA1 SeasonTV Dramas, TV Horror, TV MysteriesThe arrival of a charismatic young priest brin...
\n", 1435 | "
" 1436 | ], 1437 | "text/plain": [ 1438 | " show_id type title director \\\n", 1439 | "1 s2 TV Show Blood & Water NaN \n", 1440 | "2 s3 TV Show Ganglands Julien Leclercq \n", 1441 | "3 s4 TV Show Jailbirds New Orleans NaN \n", 1442 | "4 s5 TV Show Kota Factory NaN \n", 1443 | "5 s6 TV Show Midnight Mass Mike Flanagan \n", 1444 | "\n", 1445 | " cast country \\\n", 1446 | "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa \n", 1447 | "2 Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN \n", 1448 | "3 NaN NaN \n", 1449 | "4 Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India \n", 1450 | "5 Kate Siegel, Zach Gilford, Hamish Linklater, H... NaN \n", 1451 | "\n", 1452 | " date_added release_year rating duration \\\n", 1453 | "1 September 24, 2021 2021 TV-MA 2 Seasons \n", 1454 | "2 September 24, 2021 2021 TV-MA 1 Season \n", 1455 | "3 September 24, 2021 2021 TV-MA 1 Season \n", 1456 | "4 September 24, 2021 2021 TV-MA 2 Seasons \n", 1457 | "5 September 24, 2021 2021 TV-MA 1 Season \n", 1458 | "\n", 1459 | " listed_in \\\n", 1460 | "1 International TV Shows, TV Dramas, TV Mysteries \n", 1461 | "2 Crime TV Shows, International TV Shows, TV Act... \n", 1462 | "3 Docuseries, Reality TV \n", 1463 | "4 International TV Shows, Romantic TV Shows, TV ... \n", 1464 | "5 TV Dramas, TV Horror, TV Mysteries \n", 1465 | "\n", 1466 | " description \n", 1467 | "1 After crossing paths at a party, a Cape Town t... \n", 1468 | "2 To protect his family from a powerful drug lor... \n", 1469 | "3 Feuds, flirtations and toilet talk go down amo... \n", 1470 | "4 In a city of coaching centers known to train I... \n", 1471 | "5 The arrival of a charismatic young priest brin... " 1472 | ] 1473 | }, 1474 | "execution_count": 180, 1475 | "metadata": {}, 1476 | "output_type": "execute_result" 1477 | } 1478 | ], 1479 | "source": [ 1480 | "# Filter\n", 1481 | "df_tv_show = df[df['type'] =='TV Show']\n", 1482 | "df_tv_show.head(5)" 1483 | ] 1484 | }, 1485 | { 1486 | "cell_type": "code", 1487 | "execution_count": 181, 1488 | "id": "ae0b57ef", 1489 | "metadata": {}, 1490 | "outputs": [ 1491 | { 1492 | "data": { 1493 | "text/html": [ 1494 | "
\n", 1495 | "\n", 1508 | "\n", 1509 | " \n", 1510 | " \n", 1511 | " \n", 1512 | " \n", 1513 | " \n", 1514 | " \n", 1515 | " \n", 1516 | " \n", 1517 | " \n", 1518 | " \n", 1519 | " \n", 1520 | " \n", 1521 | " \n", 1522 | " \n", 1523 | " \n", 1524 | " \n", 1525 | " \n", 1526 | " \n", 1527 | " \n", 1528 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
\n", 1529 | "
" 1530 | ], 1531 | "text/plain": [ 1532 | "Empty DataFrame\n", 1533 | "Columns: [show_id, type, title, director, cast, country, date_added, release_year, rating, duration, listed_in, description]\n", 1534 | "Index: []" 1535 | ] 1536 | }, 1537 | "execution_count": 181, 1538 | "metadata": {}, 1539 | "output_type": "execute_result" 1540 | } 1541 | ], 1542 | "source": [ 1543 | "# Duplicates\n", 1544 | "df[df.duplicated()]" 1545 | ] 1546 | }, 1547 | { 1548 | "cell_type": "code", 1549 | "execution_count": 182, 1550 | "id": "9d6b8a10", 1551 | "metadata": {}, 1552 | "outputs": [ 1553 | { 1554 | "data": { 1555 | "text/html": [ 1556 | "
\n", 1557 | "\n", 1570 | "\n", 1571 | " \n", 1572 | " \n", 1573 | " \n", 1574 | " \n", 1575 | " \n", 1576 | " \n", 1577 | " \n", 1578 | " \n", 1579 | " \n", 1580 | " \n", 1581 | " \n", 1582 | " \n", 1583 | " \n", 1584 | " \n", 1585 | " \n", 1586 | " \n", 1587 | " \n", 1588 | " \n", 1589 | " \n", 1590 | " \n", 1591 | " \n", 1592 | " \n", 1593 | " \n", 1594 | " \n", 1595 | " \n", 1596 | " \n", 1597 | " \n", 1598 | " \n", 1599 | " \n", 1600 | " \n", 1601 | " \n", 1602 | " \n", 1603 | " \n", 1604 | " \n", 1605 | " \n", 1606 | " \n", 1607 | " \n", 1608 | " \n", 1609 | " \n", 1610 | " \n", 1611 | " \n", 1612 | " \n", 1613 | " \n", 1614 | " \n", 1615 | " \n", 1616 | " \n", 1617 | " \n", 1618 | " \n", 1619 | " \n", 1620 | " \n", 1621 | " \n", 1622 | " \n", 1623 | " \n", 1624 | " \n", 1625 | " \n", 1626 | " \n", 1627 | " \n", 1628 | " \n", 1629 | " \n", 1630 | " \n", 1631 | " \n", 1632 | " \n", 1633 | " \n", 1634 | " \n", 1635 | " \n", 1636 | " \n", 1637 | " \n", 1638 | " \n", 1639 | " \n", 1640 | " \n", 1641 | " \n", 1642 | " \n", 1643 | " \n", 1644 | " \n", 1645 | " \n", 1646 | " \n", 1647 | " \n", 1648 | " \n", 1649 | " \n", 1650 | " \n", 1651 | " \n", 1652 | " \n", 1653 | " \n", 1654 | " \n", 1655 | " \n", 1656 | " \n", 1657 | " \n", 1658 | " \n", 1659 | " \n", 1660 | " \n", 1661 | " \n", 1662 | " \n", 1663 | " \n", 1664 | " \n", 1665 | " \n", 1666 | " \n", 1667 | " \n", 1668 | " \n", 1669 | " \n", 1670 | " \n", 1671 | " \n", 1672 | " \n", 1673 | " \n", 1674 | " \n", 1675 | " \n", 1676 | " \n", 1677 | " \n", 1678 | " \n", 1679 | " \n", 1680 | " \n", 1681 | " \n", 1682 | " \n", 1683 | " \n", 1684 | " \n", 1685 | " \n", 1686 | " \n", 1687 | " \n", 1688 | " \n", 1689 | " \n", 1690 | " \n", 1691 | " \n", 1692 | " \n", 1693 | " \n", 1694 | " \n", 1695 | " \n", 1696 | " \n", 1697 | " \n", 1698 | " \n", 1699 | " \n", 1700 | " \n", 1701 | " \n", 1702 | " \n", 1703 | " \n", 1704 | " \n", 1705 | " \n", 1706 | " \n", 1707 | " \n", 1708 | " \n", 1709 | " \n", 1710 | " \n", 1711 | " \n", 1712 | " \n", 1713 | " \n", 1714 | " \n", 1715 | " \n", 1716 | " \n", 1717 | " \n", 1718 | " \n", 1719 | " \n", 1720 | " \n", 1721 | " \n", 1722 | " \n", 1723 | " \n", 1724 | " \n", 1725 | " \n", 1726 | " \n", 1727 | " \n", 1728 | " \n", 1729 | " \n", 1730 | " \n", 1731 | " \n", 1732 | " \n", 1733 | " \n", 1734 | " \n", 1735 | " \n", 1736 | " \n", 1737 | " \n", 1738 | " \n", 1739 | " \n", 1740 | " \n", 1741 | " \n", 1742 | " \n", 1743 | " \n", 1744 | " \n", 1745 | " \n", 1746 | " \n", 1747 | " \n", 1748 | " \n", 1749 | " \n", 1750 | " \n", 1751 | " \n", 1752 | " \n", 1753 | " \n", 1754 | " \n", 1755 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
0s1MovieDick Johnson Is DeadKirsten JohnsonNaNUnited StatesSeptember 25, 20212020PG-1390 minDocumentariesAs her father nears the end of his life, filmm...
1s2TV ShowBlood & WaterNaNAma Qamata, Khosi Ngema, Gail Mabalane, Thaban...South AfricaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, TV Dramas, TV MysteriesAfter crossing paths at a party, a Cape Town t...
2s3TV ShowGanglandsJulien LeclercqSami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...NaNSeptember 24, 20212021TV-MA1 SeasonCrime TV Shows, International TV Shows, TV Act...To protect his family from a powerful drug lor...
3s4TV ShowJailbirds New OrleansNaNNaNNaNSeptember 24, 20212021TV-MA1 SeasonDocuseries, Reality TVFeuds, flirtations and toilet talk go down amo...
4s5TV ShowKota FactoryNaNMayur More, Jitendra Kumar, Ranjan Raj, Alam K...IndiaSeptember 24, 20212021TV-MA2 SeasonsInternational TV Shows, Romantic TV Shows, TV ...In a city of coaching centers known to train I...
.......................................
8802s8803MovieZodiacDavid FincherMark Ruffalo, Jake Gyllenhaal, Robert Downey J...United StatesNovember 20, 20192007R158 minCult Movies, Dramas, ThrillersA political cartoonist, a crime reporter and a...
8803s8804TV ShowZombie DumbNaNNaNNaNJuly 1, 20192018TV-Y72 SeasonsKids' TV, Korean TV Shows, TV ComediesWhile living alone in a spooky town, a young g...
8804s8805MovieZombielandRuben FleischerJesse Eisenberg, Woody Harrelson, Emma Stone, ...United StatesNovember 1, 20192009R88 minComedies, Horror MoviesLooking to survive in a world taken over by zo...
8805s8806MovieZoomPeter HewittTim Allen, Courteney Cox, Chevy Chase, Kate Ma...United StatesJanuary 11, 20202006PG88 minChildren & Family Movies, ComediesDragged from civilian life, a former superhero...
8806s8807MovieZubaanMozez SinghVicky Kaushal, Sarah-Jane Dias, Raaghav Chanan...IndiaMarch 2, 20192015TV-14111 minDramas, International Movies, Music & MusicalsA scrappy but poor boy worms his way into a ty...
\n", 1756 | "

8807 rows × 12 columns

\n", 1757 | "
" 1758 | ], 1759 | "text/plain": [ 1760 | " show_id type title director \\\n", 1761 | "0 s1 Movie Dick Johnson Is Dead Kirsten Johnson \n", 1762 | "1 s2 TV Show Blood & Water NaN \n", 1763 | "2 s3 TV Show Ganglands Julien Leclercq \n", 1764 | "3 s4 TV Show Jailbirds New Orleans NaN \n", 1765 | "4 s5 TV Show Kota Factory NaN \n", 1766 | "... ... ... ... ... \n", 1767 | "8802 s8803 Movie Zodiac David Fincher \n", 1768 | "8803 s8804 TV Show Zombie Dumb NaN \n", 1769 | "8804 s8805 Movie Zombieland Ruben Fleischer \n", 1770 | "8805 s8806 Movie Zoom Peter Hewitt \n", 1771 | "8806 s8807 Movie Zubaan Mozez Singh \n", 1772 | "\n", 1773 | " cast country \\\n", 1774 | "0 NaN United States \n", 1775 | "1 Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban... South Africa \n", 1776 | "2 Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi... NaN \n", 1777 | "3 NaN NaN \n", 1778 | "4 Mayur More, Jitendra Kumar, Ranjan Raj, Alam K... India \n", 1779 | "... ... ... \n", 1780 | "8802 Mark Ruffalo, Jake Gyllenhaal, Robert Downey J... United States \n", 1781 | "8803 NaN NaN \n", 1782 | "8804 Jesse Eisenberg, Woody Harrelson, Emma Stone, ... United States \n", 1783 | "8805 Tim Allen, Courteney Cox, Chevy Chase, Kate Ma... United States \n", 1784 | "8806 Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan... India \n", 1785 | "\n", 1786 | " date_added release_year rating duration \\\n", 1787 | "0 September 25, 2021 2020 PG-13 90 min \n", 1788 | "1 September 24, 2021 2021 TV-MA 2 Seasons \n", 1789 | "2 September 24, 2021 2021 TV-MA 1 Season \n", 1790 | "3 September 24, 2021 2021 TV-MA 1 Season \n", 1791 | "4 September 24, 2021 2021 TV-MA 2 Seasons \n", 1792 | "... ... ... ... ... \n", 1793 | "8802 November 20, 2019 2007 R 158 min \n", 1794 | "8803 July 1, 2019 2018 TV-Y7 2 Seasons \n", 1795 | "8804 November 1, 2019 2009 R 88 min \n", 1796 | "8805 January 11, 2020 2006 PG 88 min \n", 1797 | "8806 March 2, 2019 2015 TV-14 111 min \n", 1798 | "\n", 1799 | " listed_in \\\n", 1800 | "0 Documentaries \n", 1801 | "1 International TV Shows, TV Dramas, TV Mysteries \n", 1802 | "2 Crime TV Shows, International TV Shows, TV Act... \n", 1803 | "3 Docuseries, Reality TV \n", 1804 | "4 International TV Shows, Romantic TV Shows, TV ... \n", 1805 | "... ... \n", 1806 | "8802 Cult Movies, Dramas, Thrillers \n", 1807 | "8803 Kids' TV, Korean TV Shows, TV Comedies \n", 1808 | "8804 Comedies, Horror Movies \n", 1809 | "8805 Children & Family Movies, Comedies \n", 1810 | "8806 Dramas, International Movies, Music & Musicals \n", 1811 | "\n", 1812 | " description \n", 1813 | "0 As her father nears the end of his life, filmm... \n", 1814 | "1 After crossing paths at a party, a Cape Town t... \n", 1815 | "2 To protect his family from a powerful drug lor... \n", 1816 | "3 Feuds, flirtations and toilet talk go down amo... \n", 1817 | "4 In a city of coaching centers known to train I... \n", 1818 | "... ... \n", 1819 | "8802 A political cartoonist, a crime reporter and a... \n", 1820 | "8803 While living alone in a spooky town, a young g... \n", 1821 | "8804 Looking to survive in a world taken over by zo... \n", 1822 | "8805 Dragged from civilian life, a former superhero... \n", 1823 | "8806 A scrappy but poor boy worms his way into a ty... \n", 1824 | "\n", 1825 | "[8807 rows x 12 columns]" 1826 | ] 1827 | }, 1828 | "execution_count": 182, 1829 | "metadata": {}, 1830 | "output_type": "execute_result" 1831 | } 1832 | ], 1833 | "source": [ 1834 | "# Removoe duplicates\n", 1835 | "df.drop_duplicates()" 1836 | ] 1837 | }, 1838 | { 1839 | "cell_type": "code", 1840 | "execution_count": 183, 1841 | "id": "636c74e5", 1842 | "metadata": {}, 1843 | "outputs": [], 1844 | "source": [ 1845 | "# Removoe duplicates inplace\n", 1846 | "df.drop_duplicates(inplace=True)" 1847 | ] 1848 | }, 1849 | { 1850 | "cell_type": "code", 1851 | "execution_count": 184, 1852 | "id": "f1cf8869", 1853 | "metadata": {}, 1854 | "outputs": [ 1855 | { 1856 | "data": { 1857 | "text/plain": [ 1858 | "show_id 0\n", 1859 | "type 0\n", 1860 | "title 0\n", 1861 | "director 2634\n", 1862 | "cast 825\n", 1863 | "country 831\n", 1864 | "date_added 10\n", 1865 | "release_year 0\n", 1866 | "rating 4\n", 1867 | "duration 3\n", 1868 | "listed_in 0\n", 1869 | "description 0\n", 1870 | "dtype: int64" 1871 | ] 1872 | }, 1873 | "execution_count": 184, 1874 | "metadata": {}, 1875 | "output_type": "execute_result" 1876 | } 1877 | ], 1878 | "source": [ 1879 | "# Show null values for each columns\n", 1880 | "df.isnull().sum()" 1881 | ] 1882 | }, 1883 | { 1884 | "cell_type": "code", 1885 | "execution_count": 185, 1886 | "id": "bdc31ed3", 1887 | "metadata": {}, 1888 | "outputs": [ 1889 | { 1890 | "data": { 1891 | "text/plain": [ 1892 | "show_id object\n", 1893 | "type object\n", 1894 | "title object\n", 1895 | "director object\n", 1896 | "cast object\n", 1897 | "country object\n", 1898 | "date_added object\n", 1899 | "release_year int64\n", 1900 | "rating object\n", 1901 | "duration object\n", 1902 | "listed_in object\n", 1903 | "description object\n", 1904 | "dtype: object" 1905 | ] 1906 | }, 1907 | "execution_count": 185, 1908 | "metadata": {}, 1909 | "output_type": "execute_result" 1910 | } 1911 | ], 1912 | "source": [ 1913 | "# Check data types\n", 1914 | "df.dtypes" 1915 | ] 1916 | }, 1917 | { 1918 | "cell_type": "code", 1919 | "execution_count": 186, 1920 | "id": "309f9910", 1921 | "metadata": {}, 1922 | "outputs": [ 1923 | { 1924 | "data": { 1925 | "text/plain": [ 1926 | "2018 1147\n", 1927 | "2017 1032\n", 1928 | "2019 1030\n", 1929 | "2020 953\n", 1930 | "2016 902\n", 1931 | " ... \n", 1932 | "1966 1\n", 1933 | "1925 1\n", 1934 | "1947 1\n", 1935 | "1959 1\n", 1936 | "1961 1\n", 1937 | "Name: release_year, Length: 74, dtype: int64" 1938 | ] 1939 | }, 1940 | "execution_count": 186, 1941 | "metadata": {}, 1942 | "output_type": "execute_result" 1943 | } 1944 | ], 1945 | "source": [ 1946 | "# Count Occurences for the release_year column\n", 1947 | "df['release_year'].value_counts()" 1948 | ] 1949 | }, 1950 | { 1951 | "cell_type": "code", 1952 | "execution_count": 187, 1953 | "id": "1bef229f", 1954 | "metadata": {}, 1955 | "outputs": [ 1956 | { 1957 | "data": { 1958 | "text/plain": [ 1959 | "" 1960 | ] 1961 | }, 1962 | "execution_count": 187, 1963 | "metadata": {}, 1964 | "output_type": "execute_result" 1965 | }, 1966 | { 1967 | "data": { 1968 | "image/png": "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\n", 1969 | "text/plain": [ 1970 | "
" 1971 | ] 1972 | }, 1973 | "metadata": { 1974 | "needs_background": "light" 1975 | }, 1976 | "output_type": "display_data" 1977 | } 1978 | ], 1979 | "source": [ 1980 | "# bar plot for first 10 values\n", 1981 | "df['release_year'].value_counts().head(10).plot(kind='bar')" 1982 | ] 1983 | }, 1984 | { 1985 | "cell_type": "code", 1986 | "execution_count": 188, 1987 | "id": "8bf320f3", 1988 | "metadata": {}, 1989 | "outputs": [ 1990 | { 1991 | "data": { 1992 | "text/plain": [ 1993 | "Rajiv Chilaka 19\n", 1994 | "Raúl Campos, Jan Suter 18\n", 1995 | "Marcus Raboy 16\n", 1996 | "Suhas Kadav 16\n", 1997 | "Jay Karas 14\n", 1998 | "Cathy Garcia-Molina 13\n", 1999 | "Martin Scorsese 12\n", 2000 | "Jay Chapman 12\n", 2001 | "Youssef Chahine 12\n", 2002 | "Steven Spielberg 11\n", 2003 | "Name: director, dtype: int64" 2004 | ] 2005 | }, 2006 | "execution_count": 188, 2007 | "metadata": {}, 2008 | "output_type": "execute_result" 2009 | } 2010 | ], 2011 | "source": [ 2012 | "df['director'].value_counts().head(10)" 2013 | ] 2014 | }, 2015 | { 2016 | "cell_type": "code", 2017 | "execution_count": 189, 2018 | "id": "154692f1", 2019 | "metadata": {}, 2020 | "outputs": [ 2021 | { 2022 | "data": { 2023 | "text/plain": [ 2024 | "type\n", 2025 | "Movie 6131\n", 2026 | "TV Show 2676\n", 2027 | "Name: type, dtype: int64" 2028 | ] 2029 | }, 2030 | "execution_count": 189, 2031 | "metadata": {}, 2032 | "output_type": "execute_result" 2033 | } 2034 | ], 2035 | "source": [ 2036 | "# TV Show vs. Movie\n", 2037 | "df.groupby('type').type.count()" 2038 | ] 2039 | }, 2040 | { 2041 | "cell_type": "code", 2042 | "execution_count": 190, 2043 | "id": "3f924521", 2044 | "metadata": {}, 2045 | "outputs": [], 2046 | "source": [ 2047 | "# Filter\n", 2048 | "df_movies_in_2000 = df[(df['type'] =='Movie') & (df['release_year'] == 2000)]" 2049 | ] 2050 | }, 2051 | { 2052 | "cell_type": "code", 2053 | "execution_count": 191, 2054 | "id": "d6841a74", 2055 | "metadata": {}, 2056 | "outputs": [ 2057 | { 2058 | "data": { 2059 | "text/html": [ 2060 | "
\n", 2061 | "\n", 2074 | "\n", 2075 | " \n", 2076 | " \n", 2077 | " \n", 2078 | " \n", 2079 | " \n", 2080 | " \n", 2081 | " \n", 2082 | " \n", 2083 | " \n", 2084 | " \n", 2085 | " \n", 2086 | " \n", 2087 | " \n", 2088 | " \n", 2089 | " \n", 2090 | " \n", 2091 | " \n", 2092 | " \n", 2093 | " \n", 2094 | " \n", 2095 | " \n", 2096 | " \n", 2097 | " \n", 2098 | " \n", 2099 | " \n", 2100 | " \n", 2101 | " \n", 2102 | " \n", 2103 | " \n", 2104 | " \n", 2105 | " \n", 2106 | " \n", 2107 | " \n", 2108 | " \n", 2109 | " \n", 2110 | " \n", 2111 | " \n", 2112 | " \n", 2113 | " \n", 2114 | " \n", 2115 | " \n", 2116 | " \n", 2117 | " \n", 2118 | " \n", 2119 | " \n", 2120 | " \n", 2121 | " \n", 2122 | " \n", 2123 | " \n", 2124 | " \n", 2125 | " \n", 2126 | " \n", 2127 | " \n", 2128 | " \n", 2129 | " \n", 2130 | " \n", 2131 | " \n", 2132 | " \n", 2133 | " \n", 2134 | " \n", 2135 | " \n", 2136 | " \n", 2137 | " \n", 2138 | " \n", 2139 | " \n", 2140 | " \n", 2141 | " \n", 2142 | " \n", 2143 | " \n", 2144 | " \n", 2145 | " \n", 2146 | " \n", 2147 | " \n", 2148 | " \n", 2149 | " \n", 2150 | " \n", 2151 | " \n", 2152 | " \n", 2153 | " \n", 2154 | " \n", 2155 | " \n", 2156 | " \n", 2157 | " \n", 2158 | " \n", 2159 | " \n", 2160 | " \n", 2161 | " \n", 2162 | " \n", 2163 | " \n", 2164 | " \n", 2165 | " \n", 2166 | " \n", 2167 | " \n", 2168 | " \n", 2169 | " \n", 2170 | " \n", 2171 | " \n", 2172 | " \n", 2173 | " \n", 2174 | " \n", 2175 | " \n", 2176 | " \n", 2177 | " \n", 2178 | " \n", 2179 | " \n", 2180 | " \n", 2181 | " \n", 2182 | " \n", 2183 | " \n", 2184 | " \n", 2185 | " \n", 2186 | " \n", 2187 | " \n", 2188 | " \n", 2189 | " \n", 2190 | " \n", 2191 | " \n", 2192 | " \n", 2193 | " \n", 2194 | " \n", 2195 | " \n", 2196 | " \n", 2197 | " \n", 2198 | " \n", 2199 | " \n", 2200 | " \n", 2201 | " \n", 2202 | " \n", 2203 | " \n", 2204 | " \n", 2205 | " \n", 2206 | " \n", 2207 | " \n", 2208 | " \n", 2209 | " \n", 2210 | " \n", 2211 | " \n", 2212 | " \n", 2213 | " \n", 2214 | " \n", 2215 | " \n", 2216 | " \n", 2217 | " \n", 2218 | " \n", 2219 | " \n", 2220 | " \n", 2221 | " \n", 2222 | " \n", 2223 | " \n", 2224 | " \n", 2225 | " \n", 2226 | " \n", 2227 | " \n", 2228 | " \n", 2229 | " \n", 2230 | " \n", 2231 | " \n", 2232 | " \n", 2233 | " \n", 2234 | " \n", 2235 | " \n", 2236 | " \n", 2237 | " \n", 2238 | " \n", 2239 | " \n", 2240 | " \n", 2241 | " \n", 2242 | " \n", 2243 | " \n", 2244 | " \n", 2245 | " \n", 2246 | " \n", 2247 | " \n", 2248 | " \n", 2249 | " \n", 2250 | " \n", 2251 | " \n", 2252 | " \n", 2253 | " \n", 2254 | " \n", 2255 | " \n", 2256 | " \n", 2257 | " \n", 2258 | " \n", 2259 | " \n", 2260 | " \n", 2261 | " \n", 2262 | " \n", 2263 | " \n", 2264 | " \n", 2265 | " \n", 2266 | " \n", 2267 | " \n", 2268 | " \n", 2269 | " \n", 2270 | " \n", 2271 | " \n", 2272 | " \n", 2273 | " \n", 2274 | " \n", 2275 | " \n", 2276 | " \n", 2277 | " \n", 2278 | " \n", 2279 | " \n", 2280 | " \n", 2281 | " \n", 2282 | " \n", 2283 | " \n", 2284 | " \n", 2285 | " \n", 2286 | " \n", 2287 | " \n", 2288 | " \n", 2289 | " \n", 2290 | " \n", 2291 | " \n", 2292 | " \n", 2293 | " \n", 2294 | " \n", 2295 | " \n", 2296 | " \n", 2297 | " \n", 2298 | " \n", 2299 | " \n", 2300 | " \n", 2301 | " \n", 2302 | " \n", 2303 | " \n", 2304 | " \n", 2305 | " \n", 2306 | " \n", 2307 | " \n", 2308 | " \n", 2309 | " \n", 2310 | " \n", 2311 | " \n", 2312 | " \n", 2313 | " \n", 2314 | " \n", 2315 | " \n", 2316 | " \n", 2317 | " \n", 2318 | " \n", 2319 | " \n", 2320 | " \n", 2321 | " \n", 2322 | " \n", 2323 | " \n", 2324 | " \n", 2325 | " \n", 2326 | " \n", 2327 | " \n", 2328 | " \n", 2329 | " \n", 2330 | " \n", 2331 | " \n", 2332 | " \n", 2333 | " \n", 2334 | " \n", 2335 | " \n", 2336 | " \n", 2337 | " \n", 2338 | " \n", 2339 | " \n", 2340 | " \n", 2341 | " \n", 2342 | " \n", 2343 | " \n", 2344 | " \n", 2345 | " \n", 2346 | " \n", 2347 | " \n", 2348 | " \n", 2349 | " \n", 2350 | " \n", 2351 | " \n", 2352 | " \n", 2353 | " \n", 2354 | " \n", 2355 | " \n", 2356 | " \n", 2357 | " \n", 2358 | " \n", 2359 | " \n", 2360 | " \n", 2361 | " \n", 2362 | " \n", 2363 | " \n", 2364 | " \n", 2365 | " \n", 2366 | " \n", 2367 | " \n", 2368 | " \n", 2369 | " \n", 2370 | " \n", 2371 | " \n", 2372 | " \n", 2373 | " \n", 2374 | " \n", 2375 | " \n", 2376 | " \n", 2377 | " \n", 2378 | " \n", 2379 | " \n", 2380 | " \n", 2381 | " \n", 2382 | " \n", 2383 | " \n", 2384 | " \n", 2385 | " \n", 2386 | " \n", 2387 | " \n", 2388 | " \n", 2389 | " \n", 2390 | " \n", 2391 | " \n", 2392 | " \n", 2393 | " \n", 2394 | " \n", 2395 | " \n", 2396 | " \n", 2397 | " \n", 2398 | " \n", 2399 | " \n", 2400 | " \n", 2401 | " \n", 2402 | " \n", 2403 | " \n", 2404 | " \n", 2405 | " \n", 2406 | " \n", 2407 | " \n", 2408 | " \n", 2409 | " \n", 2410 | " \n", 2411 | " \n", 2412 | " \n", 2413 | " \n", 2414 | " \n", 2415 | " \n", 2416 | " \n", 2417 | " \n", 2418 | " \n", 2419 | " \n", 2420 | " \n", 2421 | " \n", 2422 | " \n", 2423 | " \n", 2424 | " \n", 2425 | " \n", 2426 | " \n", 2427 | " \n", 2428 | " \n", 2429 | " \n", 2430 | " \n", 2431 | " \n", 2432 | " \n", 2433 | " \n", 2434 | " \n", 2435 | " \n", 2436 | " \n", 2437 | " \n", 2438 | " \n", 2439 | " \n", 2440 | " \n", 2441 | " \n", 2442 | " \n", 2443 | " \n", 2444 | " \n", 2445 | " \n", 2446 | " \n", 2447 | " \n", 2448 | " \n", 2449 | " \n", 2450 | " \n", 2451 | " \n", 2452 | " \n", 2453 | " \n", 2454 | " \n", 2455 | " \n", 2456 | " \n", 2457 | " \n", 2458 | " \n", 2459 | " \n", 2460 | " \n", 2461 | " \n", 2462 | " \n", 2463 | " \n", 2464 | " \n", 2465 | " \n", 2466 | " \n", 2467 | " \n", 2468 | " \n", 2469 | " \n", 2470 | " \n", 2471 | " \n", 2472 | " \n", 2473 | " \n", 2474 | " \n", 2475 | " \n", 2476 | " \n", 2477 | " \n", 2478 | " \n", 2479 | " \n", 2480 | " \n", 2481 | " \n", 2482 | " \n", 2483 | " \n", 2484 | " \n", 2485 | " \n", 2486 | " \n", 2487 | " \n", 2488 | " \n", 2489 | " \n", 2490 | " \n", 2491 | " \n", 2492 | " \n", 2493 | " \n", 2494 | " \n", 2495 | " \n", 2496 | " \n", 2497 | " \n", 2498 | " \n", 2499 | " \n", 2500 | " \n", 2501 | " \n", 2502 | " \n", 2503 | " \n", 2504 | " \n", 2505 | " \n", 2506 | " \n", 2507 | " \n", 2508 | " \n", 2509 | " \n", 2510 | " \n", 2511 | " \n", 2512 | " \n", 2513 | " \n", 2514 | " \n", 2515 | " \n", 2516 | " \n", 2517 | " \n", 2518 | " \n", 2519 | " \n", 2520 | " \n", 2521 | " \n", 2522 | " \n", 2523 | " \n", 2524 | " \n", 2525 | " \n", 2526 | " \n", 2527 | " \n", 2528 | " \n", 2529 | " \n", 2530 | " \n", 2531 | " \n", 2532 | " \n", 2533 | " \n", 2534 | " \n", 2535 | " \n", 2536 | " \n", 2537 | " \n", 2538 | " \n", 2539 | " \n", 2540 | " \n", 2541 | " \n", 2542 | " \n", 2543 | " \n", 2544 | " \n", 2545 | " \n", 2546 | " \n", 2547 | " \n", 2548 | " \n", 2549 | " \n", 2550 | " \n", 2551 | " \n", 2552 | " \n", 2553 | " \n", 2554 | " \n", 2555 | " \n", 2556 | " \n", 2557 | " \n", 2558 | " \n", 2559 | " \n", 2560 | " \n", 2561 | " \n", 2562 | " \n", 2563 | " \n", 2564 | " \n", 2565 | " \n", 2566 | " \n", 2567 | " \n", 2568 | " \n", 2569 | " \n", 2570 | " \n", 2571 | " \n", 2572 | " \n", 2573 | " \n", 2574 | " \n", 2575 | " \n", 2576 | " \n", 2577 | " \n", 2578 | " \n", 2579 | " \n", 2580 | " \n", 2581 | " \n", 2582 | " \n", 2583 | " \n", 2584 | " \n", 2585 | " \n", 2586 | " \n", 2587 | " \n", 2588 | " \n", 2589 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
180s181MovieThe Nutty Professor II: The KlumpsPeter SegalEddie Murphy, Janet Jackson, Larry Miller, Joh...NaNSeptember 1, 20212000PG-13107 minComedies, Romantic MoviesAfter getting engaged, Sherman Klump prepares ...
350s351MovieSpace CowboysClint EastwoodClint Eastwood, Tommy Lee Jones, Donald Suther...United StatesAugust 1, 20212000PG-13130 minAction & Adventure, Dramas, Sci-Fi & FantasyA retired engineer agrees to help NASA prevent...
359s360MovieThe Original Kings of ComedySpike LeeSteve Harvey, D.L. Hughley, Cedric the Enterta...United StatesAugust 1, 20212000R111 minStand-Up ComedyComedians Steve Harvey, Cedric the Entertainer...
566s567MovieCharlie's AngelsMcGCameron Diaz, Drew Barrymore, Lucy Liu, Bill M...United States, GermanyJuly 1, 20212000PG-1398 minAction & Adventure, ComediesA tight-knit trio of specially trained agents ...
593s594MovieSnow DayChris KochChris Elliott, Mark Webber, Jean Smart, Schuyl...United StatesJuly 1, 20212000PG89 minChildren & Family Movies, ComediesWhen a snow day shuts down the whole town, the...
779s780MovieBattlefield EarthRoger ChristianJohn Travolta, Barry Pepper, Forest Whitaker, ...United StatesJune 2, 20212000PG-13118 minAction & Adventure, Cult Movies, Sci-Fi & FantasyIn the year 3000, an alien race known as the P...
952s953MovieThe Whole Nine YardsJonathan LynnBruce Willis, Matthew Perry, Rosanna Arquette,...United StatesMay 1, 20212000R99 minAction & Adventure, ComediesAn unhappily married dentist becomes mixed up ...
3472s3473MovieRugrats in Paris: The MovieStig Bergqvist, Paul DemeyerElizabeth Daily, Tara Strong, Cheryl Chase, Ch...Germany, United StatesOctober 1, 20192000G79 minChildren & Family Movies, ComediesThe Rugrats take to the big screen and visit P...
4545s4546MovieMonty Python: Before the Flying CircusWill YappGraham Chapman, Eric Idle, Terry Jones, Michae...United KingdomOctober 2, 20182000TV-MA56 minComedies, DocumentariesDiscover how six seemingly ordinary but suprem...
4723s4724MovieFizaKhalid MohamedKarisma Kapoor, Jaya Bhaduri, Hrithik Roshan, ...IndiaAugust 2, 20182000TV-14163 minDramas, International Movies, Music & MusicalsFiza's brother disappears during Mumbai's horr...
4956s4957MoviePhir Bhi Dil Hai HindustaniAziz MirzaShah Rukh Khan, Juhi Chawla, Paresh Rawal, Sat...IndiaApril 1, 20182000TV-14159 minComedies, Dramas, International MoviesIn this Bollywood entertainment, two journalis...
5953s5954MovieJoseph: King of DreamsRob LaDuca, Robert C. RamirezBen Affleck, Mark Hamill, Richard Herd, Mauree...United States, France, United KingdomSeptember 27, 20112000TV-PG75 minChildren & Family Movies, Dramas, Faith & Spir...With his gift of dream interpretation and his ...
6006s6007Movie28 DaysBetty ThomasSandra Bullock, Viggo Mortensen, Dominic West,...United StatesSeptember 30, 20202000PG-13104 minComedies, DramasAfter her drunken antics result in property da...
6147s6148MovieAmerican PsychoMary HarronChristian Bale, Willem Dafoe, Jared Leto, Rees...United States, CanadaSeptember 1, 20192000R102 minComedies, Cult Movies, DramasWith chiseled good looks that belie his insani...
6154s6155MovieAn American Tail: The Treasures of Manhattan I...Larry LathamThomas Dekker, Dom DeLuise, Pat Musick, Nehemi...United StatesApril 1, 20182000G79 minChildren & Family MoviesWhen Fievel and friends go hunting for buried ...
6311s6312MovieBilly ElliotStephen DaldryJamie Bell, Gary Lewis, Stuart Wells, Billy Fa...United Kingdom, FranceFebruary 1, 20192000R110 minDramas, Independent Movies, International MoviesWhen a boy trades boxing school for ballet les...
6437s6438MovieCenter StageNicholas HytnerAmanda Schull, Zoe Saldana, Susan May Pratt, P...United StatesJanuary 1, 20202000PG-13116 minDramasVying for a spot in the American Ballet Compan...
6440s6441MovieChal Mere BhaiDavid DhawanSanjay Dutt, Salman Khan, Karisma Kapoor, Dali...IndiaDecember 31, 20192000TV-14132 minComedies, International Movies, Romantic MoviesWhen a secretary saves her tycoon boss's life,...
6543s6544MovieCrouching Tiger, Hidden DragonAng LeeChow Yun Fat, Michelle Yeoh, Zhang Ziyi, Chang...Taiwan, Hong Kong, United States, ChinaMarch 1, 20192000PG-13120 minAction & Adventure, International Movies, Roma...A young woman in ancient China longs for an ad...
6643s6644MovieDragonheart: A New BeginningDoug LeflerChristopher Masterson, Harry Van Gorkum, Rona ...United StatesJanuary 1, 20202000PG84 minAction & Adventure, Sci-Fi & FantasyThis sequel introduces Draco the dragon's offs...
6753s6754MovieFinal DestinationJames WongDevon Sawa, Ali Larter, Kerr Smith, Kristen Cl...United StatesJanuary 1, 20202000R98 minCult Movies, Horror Movies, ThrillersAfter narrowly avoiding a fatal plane explosio...
6912s6913MovieHamara Dil Aapke Paas HaiSatish KaushikAnil Kapoor, Aishwarya Rai Bachchan, Sonali Be...IndiaMarch 1, 20182000TV-14158 minDramas, International Movies, Music & MusicalsLove blooms when kind-hearted Avinash takes in...
7013s7014MovieHow the Grinch Stole ChristmasRon HowardJim Carrey, Taylor Momsen, Jeffrey Tambor, Chr...United StatesJune 1, 20172000PG105 minChildren & Family Movies, ComediesThe Grinch decides to rob Whoville of Christma...
7247s7248MovieKya KehnaKundan ShahPreity Zinta, Saif Ali Khan, Anupam Kher, Fari...IndiaApril 1, 20182000TV-PG149 minDramas, International Movies, Romantic MoviesA young university student's world is shaken a...
7316s7317MovieLittle NickySteve BrillAdam Sandler, Patricia Arquette, Harvey Keitel...United StatesDecember 1, 20202000PG-1390 minComediesThe devil sends his sweet son Nicky to Earth t...
7702s7703MoviePapa the GreatBhagyarajKrishan Kumar, Nagma, Satya Prakash, Master Bo...IndiaDecember 8, 20172000TV-PG137 minComedies, Dramas, International MoviesAfter witnessing a murder, a meek family man m...
7801s7802MoviePukarRajkumar SantoshiAnil Kapoor, Madhuri Dixit, Namrata Shirodkar,...IndiaMarch 1, 20182000TV-14165 minAction & Adventure, Dramas, International MoviesA notorious terrorist manipulates an Indian ar...
7955s7956MovieScary MovieKeenen Ivory WayansAnna Faris, Jon Abrahams, Shannon Elizabeth, S...United StatesJanuary 1, 20202000R88 minComedies, Horror MoviesThe Wayans brothers spoof some of Hollywood's ...
7962s7963MovieScream 3Wes CravenDavid Arquette, Neve Campbell, Courteney Cox, ...United StatesJuly 1, 20192000R117 minHorror MoviesThis installment of the tongue-in-cheek horror...
8196s8197MovieThe Art of WarChristian DuguayWesley Snipes, Anne Archer, Maury Chaykin, Mar...United States, CanadaJuly 1, 20202000R117 minAction & AdventureFramed for the murder of an ambassador, a form...
8305s8306MovieThe Flintstones in Viva Rock VegasBrian LevantMark Addy, Stephen Baldwin, Kristen Johnston, ...United StatesOctober 1, 20192000PG91 minChildren & Family Movies, Comedies, Romantic M...Yabba-dabba-doo! Digital dinosaurs rule the da...
8722s8723MovieWhat Lies BeneathRobert ZemeckisHarrison Ford, Michelle Pfeiffer, Diana Scarwi...United StatesJanuary 1, 20202000PG-13130 minHorror Movies, ThrillersWhen Claire Spencer starts hearing ghostly voi...
8729s8730MovieWhere the Money IsMarek KanievskaPaul Newman, Linda Fiorentino, Dermot Mulroney...Germany, United States, United Kingdom, CanadaJanuary 15, 20202000PG-1389 minAction & Adventure, Comedies, DramasPaul Newman proves he's still got prodigious a...
\n", 2590 | "
" 2591 | ], 2592 | "text/plain": [ 2593 | " show_id type title \\\n", 2594 | "180 s181 Movie The Nutty Professor II: The Klumps \n", 2595 | "350 s351 Movie Space Cowboys \n", 2596 | "359 s360 Movie The Original Kings of Comedy \n", 2597 | "566 s567 Movie Charlie's Angels \n", 2598 | "593 s594 Movie Snow Day \n", 2599 | "779 s780 Movie Battlefield Earth \n", 2600 | "952 s953 Movie The Whole Nine Yards \n", 2601 | "3472 s3473 Movie Rugrats in Paris: The Movie \n", 2602 | "4545 s4546 Movie Monty Python: Before the Flying Circus \n", 2603 | "4723 s4724 Movie Fiza \n", 2604 | "4956 s4957 Movie Phir Bhi Dil Hai Hindustani \n", 2605 | "5953 s5954 Movie Joseph: King of Dreams \n", 2606 | "6006 s6007 Movie 28 Days \n", 2607 | "6147 s6148 Movie American Psycho \n", 2608 | "6154 s6155 Movie An American Tail: The Treasures of Manhattan I... \n", 2609 | "6311 s6312 Movie Billy Elliot \n", 2610 | "6437 s6438 Movie Center Stage \n", 2611 | "6440 s6441 Movie Chal Mere Bhai \n", 2612 | "6543 s6544 Movie Crouching Tiger, Hidden Dragon \n", 2613 | "6643 s6644 Movie Dragonheart: A New Beginning \n", 2614 | "6753 s6754 Movie Final Destination \n", 2615 | "6912 s6913 Movie Hamara Dil Aapke Paas Hai \n", 2616 | "7013 s7014 Movie How the Grinch Stole Christmas \n", 2617 | "7247 s7248 Movie Kya Kehna \n", 2618 | "7316 s7317 Movie Little Nicky \n", 2619 | "7702 s7703 Movie Papa the Great \n", 2620 | "7801 s7802 Movie Pukar \n", 2621 | "7955 s7956 Movie Scary Movie \n", 2622 | "7962 s7963 Movie Scream 3 \n", 2623 | "8196 s8197 Movie The Art of War \n", 2624 | "8305 s8306 Movie The Flintstones in Viva Rock Vegas \n", 2625 | "8722 s8723 Movie What Lies Beneath \n", 2626 | "8729 s8730 Movie Where the Money Is \n", 2627 | "\n", 2628 | " director \\\n", 2629 | "180 Peter Segal \n", 2630 | "350 Clint Eastwood \n", 2631 | "359 Spike Lee \n", 2632 | "566 McG \n", 2633 | "593 Chris Koch \n", 2634 | "779 Roger Christian \n", 2635 | "952 Jonathan Lynn \n", 2636 | "3472 Stig Bergqvist, Paul Demeyer \n", 2637 | "4545 Will Yapp \n", 2638 | "4723 Khalid Mohamed \n", 2639 | "4956 Aziz Mirza \n", 2640 | "5953 Rob LaDuca, Robert C. Ramirez \n", 2641 | "6006 Betty Thomas \n", 2642 | "6147 Mary Harron \n", 2643 | "6154 Larry Latham \n", 2644 | "6311 Stephen Daldry \n", 2645 | "6437 Nicholas Hytner \n", 2646 | "6440 David Dhawan \n", 2647 | "6543 Ang Lee \n", 2648 | "6643 Doug Lefler \n", 2649 | "6753 James Wong \n", 2650 | "6912 Satish Kaushik \n", 2651 | "7013 Ron Howard \n", 2652 | "7247 Kundan Shah \n", 2653 | "7316 Steve Brill \n", 2654 | "7702 Bhagyaraj \n", 2655 | "7801 Rajkumar Santoshi \n", 2656 | "7955 Keenen Ivory Wayans \n", 2657 | "7962 Wes Craven \n", 2658 | "8196 Christian Duguay \n", 2659 | "8305 Brian Levant \n", 2660 | "8722 Robert Zemeckis \n", 2661 | "8729 Marek Kanievska \n", 2662 | "\n", 2663 | " cast \\\n", 2664 | "180 Eddie Murphy, Janet Jackson, Larry Miller, Joh... \n", 2665 | "350 Clint Eastwood, Tommy Lee Jones, Donald Suther... \n", 2666 | "359 Steve Harvey, D.L. Hughley, Cedric the Enterta... \n", 2667 | "566 Cameron Diaz, Drew Barrymore, Lucy Liu, Bill M... \n", 2668 | "593 Chris Elliott, Mark Webber, Jean Smart, Schuyl... \n", 2669 | "779 John Travolta, Barry Pepper, Forest Whitaker, ... \n", 2670 | "952 Bruce Willis, Matthew Perry, Rosanna Arquette,... \n", 2671 | "3472 Elizabeth Daily, Tara Strong, Cheryl Chase, Ch... \n", 2672 | "4545 Graham Chapman, Eric Idle, Terry Jones, Michae... \n", 2673 | "4723 Karisma Kapoor, Jaya Bhaduri, Hrithik Roshan, ... \n", 2674 | "4956 Shah Rukh Khan, Juhi Chawla, Paresh Rawal, Sat... \n", 2675 | "5953 Ben Affleck, Mark Hamill, Richard Herd, Mauree... \n", 2676 | "6006 Sandra Bullock, Viggo Mortensen, Dominic West,... \n", 2677 | "6147 Christian Bale, Willem Dafoe, Jared Leto, Rees... \n", 2678 | "6154 Thomas Dekker, Dom DeLuise, Pat Musick, Nehemi... \n", 2679 | "6311 Jamie Bell, Gary Lewis, Stuart Wells, Billy Fa... \n", 2680 | "6437 Amanda Schull, Zoe Saldana, Susan May Pratt, P... \n", 2681 | "6440 Sanjay Dutt, Salman Khan, Karisma Kapoor, Dali... \n", 2682 | "6543 Chow Yun Fat, Michelle Yeoh, Zhang Ziyi, Chang... \n", 2683 | "6643 Christopher Masterson, Harry Van Gorkum, Rona ... \n", 2684 | "6753 Devon Sawa, Ali Larter, Kerr Smith, Kristen Cl... \n", 2685 | "6912 Anil Kapoor, Aishwarya Rai Bachchan, Sonali Be... \n", 2686 | "7013 Jim Carrey, Taylor Momsen, Jeffrey Tambor, Chr... \n", 2687 | "7247 Preity Zinta, Saif Ali Khan, Anupam Kher, Fari... \n", 2688 | "7316 Adam Sandler, Patricia Arquette, Harvey Keitel... \n", 2689 | "7702 Krishan Kumar, Nagma, Satya Prakash, Master Bo... \n", 2690 | "7801 Anil Kapoor, Madhuri Dixit, Namrata Shirodkar,... \n", 2691 | "7955 Anna Faris, Jon Abrahams, Shannon Elizabeth, S... \n", 2692 | "7962 David Arquette, Neve Campbell, Courteney Cox, ... \n", 2693 | "8196 Wesley Snipes, Anne Archer, Maury Chaykin, Mar... \n", 2694 | "8305 Mark Addy, Stephen Baldwin, Kristen Johnston, ... \n", 2695 | "8722 Harrison Ford, Michelle Pfeiffer, Diana Scarwi... \n", 2696 | "8729 Paul Newman, Linda Fiorentino, Dermot Mulroney... \n", 2697 | "\n", 2698 | " country date_added \\\n", 2699 | "180 NaN September 1, 2021 \n", 2700 | "350 United States August 1, 2021 \n", 2701 | "359 United States August 1, 2021 \n", 2702 | "566 United States, Germany July 1, 2021 \n", 2703 | "593 United States July 1, 2021 \n", 2704 | "779 United States June 2, 2021 \n", 2705 | "952 United States May 1, 2021 \n", 2706 | "3472 Germany, United States October 1, 2019 \n", 2707 | "4545 United Kingdom October 2, 2018 \n", 2708 | "4723 India August 2, 2018 \n", 2709 | "4956 India April 1, 2018 \n", 2710 | "5953 United States, France, United Kingdom September 27, 2011 \n", 2711 | "6006 United States September 30, 2020 \n", 2712 | "6147 United States, Canada September 1, 2019 \n", 2713 | "6154 United States April 1, 2018 \n", 2714 | "6311 United Kingdom, France February 1, 2019 \n", 2715 | "6437 United States January 1, 2020 \n", 2716 | "6440 India December 31, 2019 \n", 2717 | "6543 Taiwan, Hong Kong, United States, China March 1, 2019 \n", 2718 | "6643 United States January 1, 2020 \n", 2719 | "6753 United States January 1, 2020 \n", 2720 | "6912 India March 1, 2018 \n", 2721 | "7013 United States June 1, 2017 \n", 2722 | "7247 India April 1, 2018 \n", 2723 | "7316 United States December 1, 2020 \n", 2724 | "7702 India December 8, 2017 \n", 2725 | "7801 India March 1, 2018 \n", 2726 | "7955 United States January 1, 2020 \n", 2727 | "7962 United States July 1, 2019 \n", 2728 | "8196 United States, Canada July 1, 2020 \n", 2729 | "8305 United States October 1, 2019 \n", 2730 | "8722 United States January 1, 2020 \n", 2731 | "8729 Germany, United States, United Kingdom, Canada January 15, 2020 \n", 2732 | "\n", 2733 | " release_year rating duration \\\n", 2734 | "180 2000 PG-13 107 min \n", 2735 | "350 2000 PG-13 130 min \n", 2736 | "359 2000 R 111 min \n", 2737 | "566 2000 PG-13 98 min \n", 2738 | "593 2000 PG 89 min \n", 2739 | "779 2000 PG-13 118 min \n", 2740 | "952 2000 R 99 min \n", 2741 | "3472 2000 G 79 min \n", 2742 | "4545 2000 TV-MA 56 min \n", 2743 | "4723 2000 TV-14 163 min \n", 2744 | "4956 2000 TV-14 159 min \n", 2745 | "5953 2000 TV-PG 75 min \n", 2746 | "6006 2000 PG-13 104 min \n", 2747 | "6147 2000 R 102 min \n", 2748 | "6154 2000 G 79 min \n", 2749 | "6311 2000 R 110 min \n", 2750 | "6437 2000 PG-13 116 min \n", 2751 | "6440 2000 TV-14 132 min \n", 2752 | "6543 2000 PG-13 120 min \n", 2753 | "6643 2000 PG 84 min \n", 2754 | "6753 2000 R 98 min \n", 2755 | "6912 2000 TV-14 158 min \n", 2756 | "7013 2000 PG 105 min \n", 2757 | "7247 2000 TV-PG 149 min \n", 2758 | "7316 2000 PG-13 90 min \n", 2759 | "7702 2000 TV-PG 137 min \n", 2760 | "7801 2000 TV-14 165 min \n", 2761 | "7955 2000 R 88 min \n", 2762 | "7962 2000 R 117 min \n", 2763 | "8196 2000 R 117 min \n", 2764 | "8305 2000 PG 91 min \n", 2765 | "8722 2000 PG-13 130 min \n", 2766 | "8729 2000 PG-13 89 min \n", 2767 | "\n", 2768 | " listed_in \\\n", 2769 | "180 Comedies, Romantic Movies \n", 2770 | "350 Action & Adventure, Dramas, Sci-Fi & Fantasy \n", 2771 | "359 Stand-Up Comedy \n", 2772 | "566 Action & Adventure, Comedies \n", 2773 | "593 Children & Family Movies, Comedies \n", 2774 | "779 Action & Adventure, Cult Movies, Sci-Fi & Fantasy \n", 2775 | "952 Action & Adventure, Comedies \n", 2776 | "3472 Children & Family Movies, Comedies \n", 2777 | "4545 Comedies, Documentaries \n", 2778 | "4723 Dramas, International Movies, Music & Musicals \n", 2779 | "4956 Comedies, Dramas, International Movies \n", 2780 | "5953 Children & Family Movies, Dramas, Faith & Spir... \n", 2781 | "6006 Comedies, Dramas \n", 2782 | "6147 Comedies, Cult Movies, Dramas \n", 2783 | "6154 Children & Family Movies \n", 2784 | "6311 Dramas, Independent Movies, International Movies \n", 2785 | "6437 Dramas \n", 2786 | "6440 Comedies, International Movies, Romantic Movies \n", 2787 | "6543 Action & Adventure, International Movies, Roma... \n", 2788 | "6643 Action & Adventure, Sci-Fi & Fantasy \n", 2789 | "6753 Cult Movies, Horror Movies, Thrillers \n", 2790 | "6912 Dramas, International Movies, Music & Musicals \n", 2791 | "7013 Children & Family Movies, Comedies \n", 2792 | "7247 Dramas, International Movies, Romantic Movies \n", 2793 | "7316 Comedies \n", 2794 | "7702 Comedies, Dramas, International Movies \n", 2795 | "7801 Action & Adventure, Dramas, International Movies \n", 2796 | "7955 Comedies, Horror Movies \n", 2797 | "7962 Horror Movies \n", 2798 | "8196 Action & Adventure \n", 2799 | "8305 Children & Family Movies, Comedies, Romantic M... \n", 2800 | "8722 Horror Movies, Thrillers \n", 2801 | "8729 Action & Adventure, Comedies, Dramas \n", 2802 | "\n", 2803 | " description \n", 2804 | "180 After getting engaged, Sherman Klump prepares ... \n", 2805 | "350 A retired engineer agrees to help NASA prevent... \n", 2806 | "359 Comedians Steve Harvey, Cedric the Entertainer... \n", 2807 | "566 A tight-knit trio of specially trained agents ... \n", 2808 | "593 When a snow day shuts down the whole town, the... \n", 2809 | "779 In the year 3000, an alien race known as the P... \n", 2810 | "952 An unhappily married dentist becomes mixed up ... \n", 2811 | "3472 The Rugrats take to the big screen and visit P... \n", 2812 | "4545 Discover how six seemingly ordinary but suprem... \n", 2813 | "4723 Fiza's brother disappears during Mumbai's horr... \n", 2814 | "4956 In this Bollywood entertainment, two journalis... \n", 2815 | "5953 With his gift of dream interpretation and his ... \n", 2816 | "6006 After her drunken antics result in property da... \n", 2817 | "6147 With chiseled good looks that belie his insani... \n", 2818 | "6154 When Fievel and friends go hunting for buried ... \n", 2819 | "6311 When a boy trades boxing school for ballet les... \n", 2820 | "6437 Vying for a spot in the American Ballet Compan... \n", 2821 | "6440 When a secretary saves her tycoon boss's life,... \n", 2822 | "6543 A young woman in ancient China longs for an ad... \n", 2823 | "6643 This sequel introduces Draco the dragon's offs... \n", 2824 | "6753 After narrowly avoiding a fatal plane explosio... \n", 2825 | "6912 Love blooms when kind-hearted Avinash takes in... \n", 2826 | "7013 The Grinch decides to rob Whoville of Christma... \n", 2827 | "7247 A young university student's world is shaken a... \n", 2828 | "7316 The devil sends his sweet son Nicky to Earth t... \n", 2829 | "7702 After witnessing a murder, a meek family man m... \n", 2830 | "7801 A notorious terrorist manipulates an Indian ar... \n", 2831 | "7955 The Wayans brothers spoof some of Hollywood's ... \n", 2832 | "7962 This installment of the tongue-in-cheek horror... \n", 2833 | "8196 Framed for the murder of an ambassador, a form... \n", 2834 | "8305 Yabba-dabba-doo! Digital dinosaurs rule the da... \n", 2835 | "8722 When Claire Spencer starts hearing ghostly voi... \n", 2836 | "8729 Paul Newman proves he's still got prodigious a... " 2837 | ] 2838 | }, 2839 | "execution_count": 191, 2840 | "metadata": {}, 2841 | "output_type": "execute_result" 2842 | } 2843 | ], 2844 | "source": [ 2845 | "df_movies_in_2000" 2846 | ] 2847 | }, 2848 | { 2849 | "cell_type": "code", 2850 | "execution_count": 192, 2851 | "id": "c72144a2", 2852 | "metadata": { 2853 | "scrolled": false 2854 | }, 2855 | "outputs": [ 2856 | { 2857 | "data": { 2858 | "text/html": [ 2859 | "
\n", 2860 | "\n", 2873 | "\n", 2874 | " \n", 2875 | " \n", 2876 | " \n", 2877 | " \n", 2878 | " \n", 2879 | " \n", 2880 | " \n", 2881 | " \n", 2882 | " \n", 2883 | " \n", 2884 | " \n", 2885 | " \n", 2886 | " \n", 2887 | " \n", 2888 | " \n", 2889 | " \n", 2890 | " \n", 2891 | " \n", 2892 | " \n", 2893 | " \n", 2894 | " \n", 2895 | " \n", 2896 | " \n", 2897 | " \n", 2898 | " \n", 2899 | " \n", 2900 | " \n", 2901 | " \n", 2902 | " \n", 2903 | " \n", 2904 | " \n", 2905 | " \n", 2906 | " \n", 2907 | " \n", 2908 | " \n", 2909 | " \n", 2910 | " \n", 2911 | " \n", 2912 | " \n", 2913 | " \n", 2914 | " \n", 2915 | " \n", 2916 | " \n", 2917 | " \n", 2918 | " \n", 2919 | " \n", 2920 | " \n", 2921 | " \n", 2922 | " \n", 2923 | " \n", 2924 | " \n", 2925 | " \n", 2926 | " \n", 2927 | " \n", 2928 | " \n", 2929 | " \n", 2930 | " \n", 2931 | " \n", 2932 | " \n", 2933 | " \n", 2934 | " \n", 2935 | " \n", 2936 | " \n", 2937 | " \n", 2938 | " \n", 2939 | " \n", 2940 | " \n", 2941 | " \n", 2942 | " \n", 2943 | " \n", 2944 | " \n", 2945 | " \n", 2946 | " \n", 2947 | " \n", 2948 | " \n", 2949 | " \n", 2950 | " \n", 2951 | " \n", 2952 | " \n", 2953 | " \n", 2954 | " \n", 2955 | " \n", 2956 | " \n", 2957 | " \n", 2958 | " \n", 2959 | " \n", 2960 | " \n", 2961 | " \n", 2962 | " \n", 2963 | " \n", 2964 | " \n", 2965 | " \n", 2966 | " \n", 2967 | " \n", 2968 | " \n", 2969 | " \n", 2970 | " \n", 2971 | " \n", 2972 | " \n", 2973 | " \n", 2974 | " \n", 2975 | " \n", 2976 | " \n", 2977 | " \n", 2978 | " \n", 2979 | " \n", 2980 | " \n", 2981 | " \n", 2982 | " \n", 2983 | " \n", 2984 | " \n", 2985 | " \n", 2986 | " \n", 2987 | " \n", 2988 | " \n", 2989 | " \n", 2990 | " \n", 2991 | " \n", 2992 | " \n", 2993 | " \n", 2994 | " \n", 2995 | " \n", 2996 | " \n", 2997 | " \n", 2998 | " \n", 2999 | " \n", 3000 | " \n", 3001 | " \n", 3002 | " \n", 3003 | " \n", 3004 | " \n", 3005 | " \n", 3006 | " \n", 3007 | " \n", 3008 | " \n", 3009 | " \n", 3010 | " \n", 3011 | " \n", 3012 | " \n", 3013 | " \n", 3014 | " \n", 3015 | " \n", 3016 | " \n", 3017 | " \n", 3018 | " \n", 3019 | " \n", 3020 | " \n", 3021 | " \n", 3022 | " \n", 3023 | " \n", 3024 | " \n", 3025 | " \n", 3026 | " \n", 3027 | " \n", 3028 | " \n", 3029 | " \n", 3030 | " \n", 3031 | " \n", 3032 | " \n", 3033 | " \n", 3034 | " \n", 3035 | " \n", 3036 | " \n", 3037 | " \n", 3038 | " \n", 3039 | " \n", 3040 | " \n", 3041 | " \n", 3042 | " \n", 3043 | " \n", 3044 | " \n", 3045 | " \n", 3046 | " \n", 3047 | " \n", 3048 | " \n", 3049 | " \n", 3050 | " \n", 3051 | " \n", 3052 | " \n", 3053 | " \n", 3054 | " \n", 3055 | " \n", 3056 | " \n", 3057 | " \n", 3058 | " \n", 3059 | " \n", 3060 | " \n", 3061 | " \n", 3062 | " \n", 3063 | " \n", 3064 | " \n", 3065 | " \n", 3066 | " \n", 3067 | " \n", 3068 | " \n", 3069 | " \n", 3070 | " \n", 3071 | " \n", 3072 | " \n", 3073 | " \n", 3074 | " \n", 3075 | " \n", 3076 | " \n", 3077 | " \n", 3078 | " \n", 3079 | " \n", 3080 | " \n", 3081 | " \n", 3082 | " \n", 3083 | " \n", 3084 | " \n", 3085 | " \n", 3086 | " \n", 3087 | " \n", 3088 | " \n", 3089 | " \n", 3090 | " \n", 3091 | " \n", 3092 | " \n", 3093 | " \n", 3094 | " \n", 3095 | " \n", 3096 | " \n", 3097 | " \n", 3098 | " \n", 3099 | " \n", 3100 | " \n", 3101 | " \n", 3102 | " \n", 3103 | " \n", 3104 | " \n", 3105 | " \n", 3106 | " \n", 3107 | " \n", 3108 | " \n", 3109 | " \n", 3110 | " \n", 3111 | " \n", 3112 | " \n", 3113 | " \n", 3114 | " \n", 3115 | " \n", 3116 | " \n", 3117 | " \n", 3118 | " \n", 3119 | " \n", 3120 | " \n", 3121 | " \n", 3122 | " \n", 3123 | " \n", 3124 | " \n", 3125 | " \n", 3126 | " \n", 3127 | " \n", 3128 | " \n", 3129 | " \n", 3130 | " \n", 3131 | " \n", 3132 | " \n", 3133 | " \n", 3134 | " \n", 3135 | " \n", 3136 | " \n", 3137 | " \n", 3138 | " \n", 3139 | " \n", 3140 | " \n", 3141 | " \n", 3142 | " \n", 3143 | " \n", 3144 | " \n", 3145 | " \n", 3146 | " \n", 3147 | " \n", 3148 | " \n", 3149 | " \n", 3150 | " \n", 3151 | " \n", 3152 | " \n", 3153 | " \n", 3154 | " \n", 3155 | " \n", 3156 | " \n", 3157 | " \n", 3158 | " \n", 3159 | " \n", 3160 | " \n", 3161 | " \n", 3162 | " \n", 3163 | " \n", 3164 | " \n", 3165 | " \n", 3166 | " \n", 3167 | " \n", 3168 | " \n", 3169 | " \n", 3170 | " \n", 3171 | " \n", 3172 | " \n", 3173 | " \n", 3174 | " \n", 3175 | " \n", 3176 | " \n", 3177 | " \n", 3178 | " \n", 3179 | " \n", 3180 | " \n", 3181 | " \n", 3182 | " \n", 3183 | " \n", 3184 | " \n", 3185 | " \n", 3186 | " \n", 3187 | " \n", 3188 | " \n", 3189 | " \n", 3190 | " \n", 3191 | " \n", 3192 | " \n", 3193 | " \n", 3194 | " \n", 3195 | " \n", 3196 | " \n", 3197 | " \n", 3198 | " \n", 3199 | " \n", 3200 | " \n", 3201 | " \n", 3202 | " \n", 3203 | " \n", 3204 | " \n", 3205 | " \n", 3206 | " \n", 3207 | " \n", 3208 | " \n", 3209 | " \n", 3210 | " \n", 3211 | " \n", 3212 | " \n", 3213 | " \n", 3214 | " \n", 3215 | " \n", 3216 | " \n", 3217 | " \n", 3218 | " \n", 3219 | " \n", 3220 | " \n", 3221 | " \n", 3222 | " \n", 3223 | " \n", 3224 | " \n", 3225 | " \n", 3226 | " \n", 3227 | " \n", 3228 | " \n", 3229 | " \n", 3230 | " \n", 3231 | " \n", 3232 | " \n", 3233 | " \n", 3234 | " \n", 3235 | " \n", 3236 | " \n", 3237 | " \n", 3238 | " \n", 3239 | " \n", 3240 | " \n", 3241 | " \n", 3242 | " \n", 3243 | " \n", 3244 | " \n", 3245 | " \n", 3246 | " \n", 3247 | " \n", 3248 | " \n", 3249 | " \n", 3250 | " \n", 3251 | " \n", 3252 | " \n", 3253 | " \n", 3254 | " \n", 3255 | " \n", 3256 | " \n", 3257 | " \n", 3258 | " \n", 3259 | " \n", 3260 | " \n", 3261 | " \n", 3262 | " \n", 3263 | " \n", 3264 | " \n", 3265 | " \n", 3266 | " \n", 3267 | " \n", 3268 | " \n", 3269 | " \n", 3270 | " \n", 3271 | " \n", 3272 | " \n", 3273 | " \n", 3274 | " \n", 3275 | " \n", 3276 | " \n", 3277 | " \n", 3278 | " \n", 3279 | " \n", 3280 | " \n", 3281 | " \n", 3282 | " \n", 3283 | " \n", 3284 | " \n", 3285 | " \n", 3286 | " \n", 3287 | " \n", 3288 | " \n", 3289 | " \n", 3290 | " \n", 3291 | " \n", 3292 | " \n", 3293 | " \n", 3294 | " \n", 3295 | " \n", 3296 | " \n", 3297 | " \n", 3298 | " \n", 3299 | " \n", 3300 | " \n", 3301 | " \n", 3302 | " \n", 3303 | " \n", 3304 | " \n", 3305 | " \n", 3306 | " \n", 3307 | " \n", 3308 | " \n", 3309 | " \n", 3310 | " \n", 3311 | " \n", 3312 | " \n", 3313 | " \n", 3314 | " \n", 3315 | " \n", 3316 | " \n", 3317 | " \n", 3318 | " \n", 3319 | " \n", 3320 | " \n", 3321 | " \n", 3322 | " \n", 3323 | " \n", 3324 | " \n", 3325 | " \n", 3326 | " \n", 3327 | " \n", 3328 | " \n", 3329 | " \n", 3330 | " \n", 3331 | " \n", 3332 | " \n", 3333 | " \n", 3334 | " \n", 3335 | " \n", 3336 | " \n", 3337 | " \n", 3338 | " \n", 3339 | " \n", 3340 | " \n", 3341 | " \n", 3342 | " \n", 3343 | " \n", 3344 | " \n", 3345 | " \n", 3346 | " \n", 3347 | " \n", 3348 | " \n", 3349 | " \n", 3350 | " \n", 3351 | " \n", 3352 | " \n", 3353 | " \n", 3354 | " \n", 3355 | " \n", 3356 | " \n", 3357 | " \n", 3358 | " \n", 3359 | " \n", 3360 | " \n", 3361 | " \n", 3362 | " \n", 3363 | " \n", 3364 | " \n", 3365 | " \n", 3366 | " \n", 3367 | " \n", 3368 | " \n", 3369 | " \n", 3370 | " \n", 3371 | " \n", 3372 | " \n", 3373 | " \n", 3374 | " \n", 3375 | " \n", 3376 | " \n", 3377 | " \n", 3378 | " \n", 3379 | " \n", 3380 | " \n", 3381 | " \n", 3382 | " \n", 3383 | " \n", 3384 | " \n", 3385 | " \n", 3386 | " \n", 3387 | " \n", 3388 | " \n", 3389 | " \n", 3390 | " \n", 3391 | " \n", 3392 | " \n", 3393 | " \n", 3394 | " \n", 3395 | " \n", 3396 | " \n", 3397 | " \n", 3398 | " \n", 3399 | " \n", 3400 | " \n", 3401 | " \n", 3402 | " \n", 3403 | " \n", 3404 | " \n", 3405 | " \n", 3406 | " \n", 3407 | " \n", 3408 | " \n", 3409 | " \n", 3410 | " \n", 3411 | " \n", 3412 | " \n", 3413 | " \n", 3414 | " \n", 3415 | " \n", 3416 | " \n", 3417 | " \n", 3418 | " \n", 3419 | " \n", 3420 | " \n", 3421 | " \n", 3422 | " \n", 3423 | " \n", 3424 | " \n", 3425 | " \n", 3426 | " \n", 3427 | " \n", 3428 | " \n", 3429 | " \n", 3430 | " \n", 3431 | " \n", 3432 | " \n", 3433 | " \n", 3434 | " \n", 3435 | " \n", 3436 | " \n", 3437 | " \n", 3438 | " \n", 3439 | " \n", 3440 | " \n", 3441 | " \n", 3442 | " \n", 3443 | " \n", 3444 | " \n", 3445 | " \n", 3446 | " \n", 3447 | " \n", 3448 | " \n", 3449 | " \n", 3450 | " \n", 3451 | " \n", 3452 | " \n", 3453 | " \n", 3454 | " \n", 3455 | " \n", 3456 | " \n", 3457 | " \n", 3458 | " \n", 3459 | " \n", 3460 | " \n", 3461 | " \n", 3462 | " \n", 3463 | " \n", 3464 | " \n", 3465 | " \n", 3466 | " \n", 3467 | " \n", 3468 | " \n", 3469 | " \n", 3470 | " \n", 3471 | " \n", 3472 | " \n", 3473 | " \n", 3474 | " \n", 3475 | " \n", 3476 | " \n", 3477 | " \n", 3478 | " \n", 3479 | " \n", 3480 | " \n", 3481 | " \n", 3482 | " \n", 3483 | " \n", 3484 | " \n", 3485 | " \n", 3486 | " \n", 3487 | " \n", 3488 | " \n", 3489 | " \n", 3490 | " \n", 3491 | " \n", 3492 | " \n", 3493 | " \n", 3494 | " \n", 3495 | " \n", 3496 | " \n", 3497 | " \n", 3498 | " \n", 3499 | " \n", 3500 | " \n", 3501 | " \n", 3502 | " \n", 3503 | " \n", 3504 | " \n", 3505 | " \n", 3506 | " \n", 3507 | " \n", 3508 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
556s557MovieThe 8th NightKim Tae-hyungLee Sung-min, Park Hae-joon, Kim You-jung, Nam...South KoreaJuly 2, 20212021TV-14116 minHorror Movies, International Movies, ThrillersWith prayer beads in one hand and an ax in the...
762s763MovieSweet & SourLee Kae-byeokJang Ki-yong, Chae Soo-bin, Jung Soo-jungSouth KoreaJune 4, 20212021TV-14103 minComedies, International Movies, Romantic MoviesFaced with real-world opportunities and challe...
1085s1086MovieNight in ParadisePark Hoon-jungUm Tae-goo, Jeon Yeo-been, Cha Seoung-won, Lee...South KoreaApril 9, 20212021TV-MA132 minDramas, International MoviesHiding out in Jeju Island following a brutal t...
1342s1343MovieSpace SweepersJo Sung-heeSong Joong-ki, Kim Tae-ri, Jin Sun-kyu, Yoo Ha...South KoreaFebruary 5, 20212021TV-MA138 minAction & Adventure, Dramas, International MoviesChasing after space debris and faraway dreams ...
1412s1413MovieWish YouSung Do-junKang In-soo, Lee Sang, Soo-binSouth KoreaJanuary 15, 20212021TV-PG102 minDramas, International Movies, LGBTQ MoviesSinging and dreaming together, a talented sing...
1468s1469MovieWhat Happened to Mr. Cha?Kim Dong-kyuCha In-pyo, Cho Dal-hwan, Song Jae-ryongSouth KoreaJanuary 1, 20212021TV-MA102 minComedies, International MoviesWith the peak of his career long behind him, a...
1643s1644MovieThe CallLee Chung-hyunPark Shin-hye, Jun Jong-seo, Kim Sung-ryoung, ...South KoreaNovember 27, 20202020TV-MA112 minInternational Movies, ThrillersConnected by phone in the same home but 20 yea...
2036s2037Movie#AliveCho IlYoo Ah-in, Park Shin-hyeSouth KoreaSeptember 8, 20202020TV-MA99 minHorror Movies, International Movies, ThrillersAs a grisly virus rampages a city, a lone man ...
2215s2216MovieThe Larva Island MovieAhn Byoung-wookHong Bum-ki, Eddy Lee, Kang Shi-hyun, Ahn Hyo-...South KoreaJuly 23, 20202020TV-Y791 minChildren & Family Movies, ComediesIn this movie sequel to the hit series, Chuck ...
2410s2411MovieThe Witch: Part 1 - The SubversionPark Hoon-jungKim Da-mi, Cho Min-soo, Choi Woo-shik, Park He...South KoreaJune 10, 20202018TV-MA126 minAction & Adventure, International Movies, Sci-...The life of a seemingly ordinary high school s...
2650s2651MovieTime to HuntYoon Sung-hyunLee Je-hoon, Ahn Jae-hong, Choi Woo-shik, Park...South KoreaApril 23, 20202020TV-MA135 minInternational Movies, ThrillersWanting to leave their dystopian world behind ...
3308s3309MovieTune in for LoveJung Ji-wooKim Go-eun, Jung Hae-in, Park Hae-joon, Kim Gu...South KoreaNovember 5, 20192019TV-MA123 minDramas, International Movies, Romantic MoviesA student and a reticent teen first meet at a ...
3419s3420MoviePark Na-rae: Glamour WarningKim Joo-hyungPark Na-raeSouth KoreaOctober 16, 20192019TV-MA63 minStand-Up ComedyKorea’s beloved comedian and favorite big sist...
3793s3794MovieSvaha: The Sixth FingerJang Jae-hyunLee Jung-jae, Park Jung-min, Lee Jae-in, Yoo J...South KoreaMay 30, 20192019TV-MA123 minInternational Movies, ThrillersA minister who researches religious cults turn...
3865s3866MovieJo Pil-ho: The Dawning RageLee Jeong-beomLee Sun-kyun, Jeon So-nee, Park Hae-joon, Song...South KoreaMay 3, 20192018TV-MA128 minDramas, International Movies, ThrillersOn the run from a dogged internal affairs agen...
4091s4092MovieThe Drug KingWoo Min-hoSong Kang-ho, Cho Jung-seok, Bae Doona, Kim So...South KoreaFebruary 21, 20192018TV-MA139 minDramas, International Movies, ThrillersA petty smuggler from Busan dives headfirst in...
4154s4155MovieHigh SocietyByun HyukPark Hae-il, Su Ae, Yoon Je-moon, Ra Mi-ran, L...South KoreaJanuary 29, 20192018TV-MA137 minDramas, International MoviesA deputy curator of a chaebol-funded art galle...
4192s4193MovieRevengerLee Seung-wonBruce Khan, Park Hee-soon, Yoon Jin-seo, Kim I...South KoreaJanuary 15, 20192018TV-MA102 minAction & Adventure, International MoviesHell-bent on avenging the murder of his family...
4493s4494MovieIllang: The Wolf BrigadeKim Jee-woonGang Dong-won, Han Hyo-joo, Jung Woo-sung, Kim...South KoreaOctober 19, 20182018TV-MA140 minAction & Adventure, International Movies, Sci-...In 2029, the elite police squad Illang combats...
4699s4700MovieYoo Byung Jae: Discomfort ZoneY. Joon ChungYoo Byung JaeSouth KoreaAugust 17, 20182018TV-MA54 minStand-Up ComedyDaring comedian Yoo Byung-jae connects critici...
4918s4919MoviePsychokinesisSang-ho YeonRyu Seung-ryong, Shim Eun-kyung, Jung-min Park...South KoreaApril 25, 20182018TV-MA102 minAction & Adventure, Comedies, International Mo...Suddenly possessed with supernatural powers, a...
4987s4988MovieSteel RainYang Woo-seokWoo-sung Jung, Do-won Kwak, Kap-soo Kim, Woo-j...South KoreaMarch 14, 20182018TV-MA140 minAction & Adventure, Dramas, International MoviesAmid a coup, a North Korean agent escapes sout...
5022s5023Movie반드시 잡는다Hong-seon KimBaek Yoon-sikSouth KoreaFebruary 28, 20182017TV-MA110 minDramas, International Movies, ThrillersAfter people in his town start turning up dead...
5031s5032MovieForgottenHang-Jun JangHa-neul Kang, Moo-Yul Kim, Young-hee Na, Seong...South KoreaFebruary 21, 20182017TV-MA109 minDramas, International Movies, ThrillersWhen his abducted brother returns seemingly a ...
5051s5052MovieThe BrosYou-Jeong ChangDong-hwi Lee, Dong-seok Ma, Ha-Nui Lee, Woo-ji...South KoreaFebruary 2, 20182017TV-14102 minComedies, Dramas, International MoviesReunited in their hometown for their father's ...
5085s5086MovieThe Reservoir GameChoi Jin-seongNaNSouth KoreaJanuary 12, 20182017TV-MA100 minDocumentaries, International MoviesAn investigative reporter seeks to expose the ...
5235s5236Movie26 YearsGeun-hyun ChoGoo Jin, Hye-jin Han, Soo-bin Bae, Seul-ong Im...South KoreaOctober 1, 20172012TV-MA135 minDramas, International Movies, ThrillersTwenty-six years after the 1980 massacre at Gw...
5446s5447MovieLucid DreamJun-seong KimKo Soo, Kyung-gu Seol, Yu-chun Park, Hye-jung ...South KoreaJune 2, 20172017TV-MA102 minInternational Movies, Sci-Fi & Fantasy, ThrillersAfter searching for his abducted son for three...
5568s5569MoviePandoraJung-woo ParkNam-gil Kim, Young-ae Kim, Jeong-hee Moon, Joo...South KoreaMarch 17, 20172016TV-MA137 minDramas, International Movies, ThrillersWhen an earthquake hits a Korean village housi...
5997s5998Movie2015 Dream ConcertNaN4Minute, B1A4, BtoB, ELSIE, EXID, EXO, Got7, I...South KoreaApril 28, 20172015TV-PG107 minInternational Movies, Music & MusicalsThe world's biggest K-pop festival marked its ...
6190s6191MovieAsura: The City of MadnessSung-soo KimJung-min Hwang, Do-won Kwak, Man-sik Jung, Woo...South KoreaFebruary 15, 20182016NR133 minAction & Adventure, Dramas, International MoviesCaught between a corrupt mayor and a prosecuto...
7420s7421MovieMasterUi-seok JoByung-hun Lee, Dong-won Gang, Woo-bin Kim, Ji-...South KoreaFebruary 1, 20182016NR143 minAction & Adventure, International MoviesNeeding hard evidence to convict a company cha...
7440s7441MovieMemoir of a MurdererShin-yeon WonKyung-gu Seol, Nam-gil Kim, Seol-hyun Kim, Dal...South KoreaFebruary 19, 20182017TV-MA118 minDramas, International Movies, ThrillersHiding his own murderous past, a man suffering...
7670s7671MovieOperation ChromiteJohn H. LeeJung-jae Lee, Beom-su Lee, Liam Neeson, Se-yeo...South KoreaJanuary 15, 20182016NR111 minAction & Adventure, Dramas, International MoviesTo pave the way for a major amphibious invasio...
7831s7832MovieRampantKim Sung-hoonHyun Bin, Jang Dong-gun, Jo Woo-jin, Jeong Man...South KoreaMay 26, 20192018TV-MA122 minAction & Adventure, Horror Movies, Internation...When Prince Lee Cheong returns to Joseon after...
8186s8187MovieThe Age of ShadowsJee-woon KimKang-ho Song, Gong Yoo, Ji-min Han, Tae-goo Eo...South KoreaJanuary 1, 20182016TV-MA140 minAction & Adventure, Dramas, International MoviesFormerly a Korean resistance fighter, a police...
8330s8331MovieThe Great BattleNaNZo In-sung, Nam Joo-hyuk, Park Sung-woong, Bae...South KoreaApril 8, 20192018TV-MA136 minAction & Adventure, Dramas, International MoviesIn seventh-century Korea, the commander of Ans...
8418s8419MovieThe MayorPark In-jeMin-sik Choi, Do-won Kwak, Eun-kyung Shim, So-...South KoreaSeptember 30, 20172017TV-MA130 minDramas, International MoviesWith the presidency in mind, the incumbent may...
8463s8464MovieThe President's BarberChan-sang LimKang-ho Song, So-ri Moon, Jae-eung Lee, Yeong-...South KoreaOctober 1, 20172004TV-MA116 minComedies, Dramas, International MoviesThe personal barber to the president, as well ...
8467s8468MovieThe PrisonNa HyeonSuk-kyu Han, Rae-won Kim, Kyeong-yeong Lee, Wo...South KoreaNovember 18, 20172017TV-MA125 minAction & Adventure, Dramas, International MoviesA cop-turned-convict discovers a crime syndica...
8613s8614MovieTrain to BusanSang-ho YeonGong Yoo, Yu-mi Jung, Dong-seok Ma, Soo-an Kim...South KoreaMarch 18, 20172016TV-MA118 minAction & Adventure, Horror Movies, Internation...As a zombie outbreak sweeps the country, a dad...
\n", 3509 | "
" 3510 | ], 3511 | "text/plain": [ 3512 | " show_id type title director \\\n", 3513 | "556 s557 Movie The 8th Night Kim Tae-hyung \n", 3514 | "762 s763 Movie Sweet & Sour Lee Kae-byeok \n", 3515 | "1085 s1086 Movie Night in Paradise Park Hoon-jung \n", 3516 | "1342 s1343 Movie Space Sweepers Jo Sung-hee \n", 3517 | "1412 s1413 Movie Wish You Sung Do-jun \n", 3518 | "1468 s1469 Movie What Happened to Mr. Cha? Kim Dong-kyu \n", 3519 | "1643 s1644 Movie The Call Lee Chung-hyun \n", 3520 | "2036 s2037 Movie #Alive Cho Il \n", 3521 | "2215 s2216 Movie The Larva Island Movie Ahn Byoung-wook \n", 3522 | "2410 s2411 Movie The Witch: Part 1 - The Subversion Park Hoon-jung \n", 3523 | "2650 s2651 Movie Time to Hunt Yoon Sung-hyun \n", 3524 | "3308 s3309 Movie Tune in for Love Jung Ji-woo \n", 3525 | "3419 s3420 Movie Park Na-rae: Glamour Warning Kim Joo-hyung \n", 3526 | "3793 s3794 Movie Svaha: The Sixth Finger Jang Jae-hyun \n", 3527 | "3865 s3866 Movie Jo Pil-ho: The Dawning Rage Lee Jeong-beom \n", 3528 | "4091 s4092 Movie The Drug King Woo Min-ho \n", 3529 | "4154 s4155 Movie High Society Byun Hyuk \n", 3530 | "4192 s4193 Movie Revenger Lee Seung-won \n", 3531 | "4493 s4494 Movie Illang: The Wolf Brigade Kim Jee-woon \n", 3532 | "4699 s4700 Movie Yoo Byung Jae: Discomfort Zone Y. Joon Chung \n", 3533 | "4918 s4919 Movie Psychokinesis Sang-ho Yeon \n", 3534 | "4987 s4988 Movie Steel Rain Yang Woo-seok \n", 3535 | "5022 s5023 Movie 반드시 잡는다 Hong-seon Kim \n", 3536 | "5031 s5032 Movie Forgotten Hang-Jun Jang \n", 3537 | "5051 s5052 Movie The Bros You-Jeong Chang \n", 3538 | "5085 s5086 Movie The Reservoir Game Choi Jin-seong \n", 3539 | "5235 s5236 Movie 26 Years Geun-hyun Cho \n", 3540 | "5446 s5447 Movie Lucid Dream Jun-seong Kim \n", 3541 | "5568 s5569 Movie Pandora Jung-woo Park \n", 3542 | "5997 s5998 Movie 2015 Dream Concert NaN \n", 3543 | "6190 s6191 Movie Asura: The City of Madness Sung-soo Kim \n", 3544 | "7420 s7421 Movie Master Ui-seok Jo \n", 3545 | "7440 s7441 Movie Memoir of a Murderer Shin-yeon Won \n", 3546 | "7670 s7671 Movie Operation Chromite John H. Lee \n", 3547 | "7831 s7832 Movie Rampant Kim Sung-hoon \n", 3548 | "8186 s8187 Movie The Age of Shadows Jee-woon Kim \n", 3549 | "8330 s8331 Movie The Great Battle NaN \n", 3550 | "8418 s8419 Movie The Mayor Park In-je \n", 3551 | "8463 s8464 Movie The President's Barber Chan-sang Lim \n", 3552 | "8467 s8468 Movie The Prison Na Hyeon \n", 3553 | "8613 s8614 Movie Train to Busan Sang-ho Yeon \n", 3554 | "\n", 3555 | " cast country \\\n", 3556 | "556 Lee Sung-min, Park Hae-joon, Kim You-jung, Nam... South Korea \n", 3557 | "762 Jang Ki-yong, Chae Soo-bin, Jung Soo-jung South Korea \n", 3558 | "1085 Um Tae-goo, Jeon Yeo-been, Cha Seoung-won, Lee... South Korea \n", 3559 | "1342 Song Joong-ki, Kim Tae-ri, Jin Sun-kyu, Yoo Ha... South Korea \n", 3560 | "1412 Kang In-soo, Lee Sang, Soo-bin South Korea \n", 3561 | "1468 Cha In-pyo, Cho Dal-hwan, Song Jae-ryong South Korea \n", 3562 | "1643 Park Shin-hye, Jun Jong-seo, Kim Sung-ryoung, ... South Korea \n", 3563 | "2036 Yoo Ah-in, Park Shin-hye South Korea \n", 3564 | "2215 Hong Bum-ki, Eddy Lee, Kang Shi-hyun, Ahn Hyo-... South Korea \n", 3565 | "2410 Kim Da-mi, Cho Min-soo, Choi Woo-shik, Park He... South Korea \n", 3566 | "2650 Lee Je-hoon, Ahn Jae-hong, Choi Woo-shik, Park... South Korea \n", 3567 | "3308 Kim Go-eun, Jung Hae-in, Park Hae-joon, Kim Gu... South Korea \n", 3568 | "3419 Park Na-rae South Korea \n", 3569 | "3793 Lee Jung-jae, Park Jung-min, Lee Jae-in, Yoo J... South Korea \n", 3570 | "3865 Lee Sun-kyun, Jeon So-nee, Park Hae-joon, Song... South Korea \n", 3571 | "4091 Song Kang-ho, Cho Jung-seok, Bae Doona, Kim So... South Korea \n", 3572 | "4154 Park Hae-il, Su Ae, Yoon Je-moon, Ra Mi-ran, L... South Korea \n", 3573 | "4192 Bruce Khan, Park Hee-soon, Yoon Jin-seo, Kim I... South Korea \n", 3574 | "4493 Gang Dong-won, Han Hyo-joo, Jung Woo-sung, Kim... South Korea \n", 3575 | "4699 Yoo Byung Jae South Korea \n", 3576 | "4918 Ryu Seung-ryong, Shim Eun-kyung, Jung-min Park... South Korea \n", 3577 | "4987 Woo-sung Jung, Do-won Kwak, Kap-soo Kim, Woo-j... South Korea \n", 3578 | "5022 Baek Yoon-sik South Korea \n", 3579 | "5031 Ha-neul Kang, Moo-Yul Kim, Young-hee Na, Seong... South Korea \n", 3580 | "5051 Dong-hwi Lee, Dong-seok Ma, Ha-Nui Lee, Woo-ji... South Korea \n", 3581 | "5085 NaN South Korea \n", 3582 | "5235 Goo Jin, Hye-jin Han, Soo-bin Bae, Seul-ong Im... South Korea \n", 3583 | "5446 Ko Soo, Kyung-gu Seol, Yu-chun Park, Hye-jung ... South Korea \n", 3584 | "5568 Nam-gil Kim, Young-ae Kim, Jeong-hee Moon, Joo... South Korea \n", 3585 | "5997 4Minute, B1A4, BtoB, ELSIE, EXID, EXO, Got7, I... South Korea \n", 3586 | "6190 Jung-min Hwang, Do-won Kwak, Man-sik Jung, Woo... South Korea \n", 3587 | "7420 Byung-hun Lee, Dong-won Gang, Woo-bin Kim, Ji-... South Korea \n", 3588 | "7440 Kyung-gu Seol, Nam-gil Kim, Seol-hyun Kim, Dal... South Korea \n", 3589 | "7670 Jung-jae Lee, Beom-su Lee, Liam Neeson, Se-yeo... South Korea \n", 3590 | "7831 Hyun Bin, Jang Dong-gun, Jo Woo-jin, Jeong Man... South Korea \n", 3591 | "8186 Kang-ho Song, Gong Yoo, Ji-min Han, Tae-goo Eo... South Korea \n", 3592 | "8330 Zo In-sung, Nam Joo-hyuk, Park Sung-woong, Bae... South Korea \n", 3593 | "8418 Min-sik Choi, Do-won Kwak, Eun-kyung Shim, So-... South Korea \n", 3594 | "8463 Kang-ho Song, So-ri Moon, Jae-eung Lee, Yeong-... South Korea \n", 3595 | "8467 Suk-kyu Han, Rae-won Kim, Kyeong-yeong Lee, Wo... South Korea \n", 3596 | "8613 Gong Yoo, Yu-mi Jung, Dong-seok Ma, Soo-an Kim... South Korea \n", 3597 | "\n", 3598 | " date_added release_year rating duration \\\n", 3599 | "556 July 2, 2021 2021 TV-14 116 min \n", 3600 | "762 June 4, 2021 2021 TV-14 103 min \n", 3601 | "1085 April 9, 2021 2021 TV-MA 132 min \n", 3602 | "1342 February 5, 2021 2021 TV-MA 138 min \n", 3603 | "1412 January 15, 2021 2021 TV-PG 102 min \n", 3604 | "1468 January 1, 2021 2021 TV-MA 102 min \n", 3605 | "1643 November 27, 2020 2020 TV-MA 112 min \n", 3606 | "2036 September 8, 2020 2020 TV-MA 99 min \n", 3607 | "2215 July 23, 2020 2020 TV-Y7 91 min \n", 3608 | "2410 June 10, 2020 2018 TV-MA 126 min \n", 3609 | "2650 April 23, 2020 2020 TV-MA 135 min \n", 3610 | "3308 November 5, 2019 2019 TV-MA 123 min \n", 3611 | "3419 October 16, 2019 2019 TV-MA 63 min \n", 3612 | "3793 May 30, 2019 2019 TV-MA 123 min \n", 3613 | "3865 May 3, 2019 2018 TV-MA 128 min \n", 3614 | "4091 February 21, 2019 2018 TV-MA 139 min \n", 3615 | "4154 January 29, 2019 2018 TV-MA 137 min \n", 3616 | "4192 January 15, 2019 2018 TV-MA 102 min \n", 3617 | "4493 October 19, 2018 2018 TV-MA 140 min \n", 3618 | "4699 August 17, 2018 2018 TV-MA 54 min \n", 3619 | "4918 April 25, 2018 2018 TV-MA 102 min \n", 3620 | "4987 March 14, 2018 2018 TV-MA 140 min \n", 3621 | "5022 February 28, 2018 2017 TV-MA 110 min \n", 3622 | "5031 February 21, 2018 2017 TV-MA 109 min \n", 3623 | "5051 February 2, 2018 2017 TV-14 102 min \n", 3624 | "5085 January 12, 2018 2017 TV-MA 100 min \n", 3625 | "5235 October 1, 2017 2012 TV-MA 135 min \n", 3626 | "5446 June 2, 2017 2017 TV-MA 102 min \n", 3627 | "5568 March 17, 2017 2016 TV-MA 137 min \n", 3628 | "5997 April 28, 2017 2015 TV-PG 107 min \n", 3629 | "6190 February 15, 2018 2016 NR 133 min \n", 3630 | "7420 February 1, 2018 2016 NR 143 min \n", 3631 | "7440 February 19, 2018 2017 TV-MA 118 min \n", 3632 | "7670 January 15, 2018 2016 NR 111 min \n", 3633 | "7831 May 26, 2019 2018 TV-MA 122 min \n", 3634 | "8186 January 1, 2018 2016 TV-MA 140 min \n", 3635 | "8330 April 8, 2019 2018 TV-MA 136 min \n", 3636 | "8418 September 30, 2017 2017 TV-MA 130 min \n", 3637 | "8463 October 1, 2017 2004 TV-MA 116 min \n", 3638 | "8467 November 18, 2017 2017 TV-MA 125 min \n", 3639 | "8613 March 18, 2017 2016 TV-MA 118 min \n", 3640 | "\n", 3641 | " listed_in \\\n", 3642 | "556 Horror Movies, International Movies, Thrillers \n", 3643 | "762 Comedies, International Movies, Romantic Movies \n", 3644 | "1085 Dramas, International Movies \n", 3645 | "1342 Action & Adventure, Dramas, International Movies \n", 3646 | "1412 Dramas, International Movies, LGBTQ Movies \n", 3647 | "1468 Comedies, International Movies \n", 3648 | "1643 International Movies, Thrillers \n", 3649 | "2036 Horror Movies, International Movies, Thrillers \n", 3650 | "2215 Children & Family Movies, Comedies \n", 3651 | "2410 Action & Adventure, International Movies, Sci-... \n", 3652 | "2650 International Movies, Thrillers \n", 3653 | "3308 Dramas, International Movies, Romantic Movies \n", 3654 | "3419 Stand-Up Comedy \n", 3655 | "3793 International Movies, Thrillers \n", 3656 | "3865 Dramas, International Movies, Thrillers \n", 3657 | "4091 Dramas, International Movies, Thrillers \n", 3658 | "4154 Dramas, International Movies \n", 3659 | "4192 Action & Adventure, International Movies \n", 3660 | "4493 Action & Adventure, International Movies, Sci-... \n", 3661 | "4699 Stand-Up Comedy \n", 3662 | "4918 Action & Adventure, Comedies, International Mo... \n", 3663 | "4987 Action & Adventure, Dramas, International Movies \n", 3664 | "5022 Dramas, International Movies, Thrillers \n", 3665 | "5031 Dramas, International Movies, Thrillers \n", 3666 | "5051 Comedies, Dramas, International Movies \n", 3667 | "5085 Documentaries, International Movies \n", 3668 | "5235 Dramas, International Movies, Thrillers \n", 3669 | "5446 International Movies, Sci-Fi & Fantasy, Thrillers \n", 3670 | "5568 Dramas, International Movies, Thrillers \n", 3671 | "5997 International Movies, Music & Musicals \n", 3672 | "6190 Action & Adventure, Dramas, International Movies \n", 3673 | "7420 Action & Adventure, International Movies \n", 3674 | "7440 Dramas, International Movies, Thrillers \n", 3675 | "7670 Action & Adventure, Dramas, International Movies \n", 3676 | "7831 Action & Adventure, Horror Movies, Internation... \n", 3677 | "8186 Action & Adventure, Dramas, International Movies \n", 3678 | "8330 Action & Adventure, Dramas, International Movies \n", 3679 | "8418 Dramas, International Movies \n", 3680 | "8463 Comedies, Dramas, International Movies \n", 3681 | "8467 Action & Adventure, Dramas, International Movies \n", 3682 | "8613 Action & Adventure, Horror Movies, Internation... \n", 3683 | "\n", 3684 | " description \n", 3685 | "556 With prayer beads in one hand and an ax in the... \n", 3686 | "762 Faced with real-world opportunities and challe... \n", 3687 | "1085 Hiding out in Jeju Island following a brutal t... \n", 3688 | "1342 Chasing after space debris and faraway dreams ... \n", 3689 | "1412 Singing and dreaming together, a talented sing... \n", 3690 | "1468 With the peak of his career long behind him, a... \n", 3691 | "1643 Connected by phone in the same home but 20 yea... \n", 3692 | "2036 As a grisly virus rampages a city, a lone man ... \n", 3693 | "2215 In this movie sequel to the hit series, Chuck ... \n", 3694 | "2410 The life of a seemingly ordinary high school s... \n", 3695 | "2650 Wanting to leave their dystopian world behind ... \n", 3696 | "3308 A student and a reticent teen first meet at a ... \n", 3697 | "3419 Korea’s beloved comedian and favorite big sist... \n", 3698 | "3793 A minister who researches religious cults turn... \n", 3699 | "3865 On the run from a dogged internal affairs agen... \n", 3700 | "4091 A petty smuggler from Busan dives headfirst in... \n", 3701 | "4154 A deputy curator of a chaebol-funded art galle... \n", 3702 | "4192 Hell-bent on avenging the murder of his family... \n", 3703 | "4493 In 2029, the elite police squad Illang combats... \n", 3704 | "4699 Daring comedian Yoo Byung-jae connects critici... \n", 3705 | "4918 Suddenly possessed with supernatural powers, a... \n", 3706 | "4987 Amid a coup, a North Korean agent escapes sout... \n", 3707 | "5022 After people in his town start turning up dead... \n", 3708 | "5031 When his abducted brother returns seemingly a ... \n", 3709 | "5051 Reunited in their hometown for their father's ... \n", 3710 | "5085 An investigative reporter seeks to expose the ... \n", 3711 | "5235 Twenty-six years after the 1980 massacre at Gw... \n", 3712 | "5446 After searching for his abducted son for three... \n", 3713 | "5568 When an earthquake hits a Korean village housi... \n", 3714 | "5997 The world's biggest K-pop festival marked its ... \n", 3715 | "6190 Caught between a corrupt mayor and a prosecuto... \n", 3716 | "7420 Needing hard evidence to convict a company cha... \n", 3717 | "7440 Hiding his own murderous past, a man suffering... \n", 3718 | "7670 To pave the way for a major amphibious invasio... \n", 3719 | "7831 When Prince Lee Cheong returns to Joseon after... \n", 3720 | "8186 Formerly a Korean resistance fighter, a police... \n", 3721 | "8330 In seventh-century Korea, the commander of Ans... \n", 3722 | "8418 With the presidency in mind, the incumbent may... \n", 3723 | "8463 The personal barber to the president, as well ... \n", 3724 | "8467 A cop-turned-convict discovers a crime syndica... \n", 3725 | "8613 As a zombie outbreak sweeps the country, a dad... " 3726 | ] 3727 | }, 3728 | "execution_count": 192, 3729 | "metadata": {}, 3730 | "output_type": "execute_result" 3731 | } 3732 | ], 3733 | "source": [ 3734 | "# Filter\n", 3735 | "df['country'].value_counts()\n", 3736 | "df[(df['country'] =='South Korea') & (df['type'] =='Movie')]" 3737 | ] 3738 | }, 3739 | { 3740 | "cell_type": "code", 3741 | "execution_count": 193, 3742 | "id": "3f85e148", 3743 | "metadata": {}, 3744 | "outputs": [ 3745 | { 3746 | "data": { 3747 | "text/plain": [ 3748 | "(8807, 12)" 3749 | ] 3750 | }, 3751 | "execution_count": 193, 3752 | "metadata": {}, 3753 | "output_type": "execute_result" 3754 | } 3755 | ], 3756 | "source": [ 3757 | "df.shape" 3758 | ] 3759 | }, 3760 | { 3761 | "cell_type": "code", 3762 | "execution_count": 194, 3763 | "id": "c18bbdb7", 3764 | "metadata": {}, 3765 | "outputs": [], 3766 | "source": [ 3767 | "# Drop null values\n", 3768 | "data_not_null = df.dropna()" 3769 | ] 3770 | }, 3771 | { 3772 | "cell_type": "code", 3773 | "execution_count": 195, 3774 | "id": "ce89f5db", 3775 | "metadata": {}, 3776 | "outputs": [ 3777 | { 3778 | "data": { 3779 | "text/plain": [ 3780 | "(5332, 12)" 3781 | ] 3782 | }, 3783 | "execution_count": 195, 3784 | "metadata": {}, 3785 | "output_type": "execute_result" 3786 | } 3787 | ], 3788 | "source": [ 3789 | "data_not_null.shape" 3790 | ] 3791 | }, 3792 | { 3793 | "cell_type": "code", 3794 | "execution_count": 196, 3795 | "id": "619d2cc4", 3796 | "metadata": {}, 3797 | "outputs": [ 3798 | { 3799 | "data": { 3800 | "text/html": [ 3801 | "
\n", 3802 | "\n", 3815 | "\n", 3816 | " \n", 3817 | " \n", 3818 | " \n", 3819 | " \n", 3820 | " \n", 3821 | " \n", 3822 | " \n", 3823 | " \n", 3824 | " \n", 3825 | " \n", 3826 | " \n", 3827 | " \n", 3828 | " \n", 3829 | " \n", 3830 | " \n", 3831 | " \n", 3832 | " \n", 3833 | " \n", 3834 | " \n", 3835 | " \n", 3836 | " \n", 3837 | " \n", 3838 | " \n", 3839 | " \n", 3840 | " \n", 3841 | " \n", 3842 | " \n", 3843 | " \n", 3844 | " \n", 3845 | " \n", 3846 | " \n", 3847 | " \n", 3848 | " \n", 3849 | " \n", 3850 | " \n", 3851 | " \n", 3852 | " \n", 3853 | " \n", 3854 | " \n", 3855 | " \n", 3856 | " \n", 3857 | " \n", 3858 | " \n", 3859 | " \n", 3860 | " \n", 3861 | " \n", 3862 | " \n", 3863 | " \n", 3864 | " \n", 3865 | " \n", 3866 | " \n", 3867 | " \n", 3868 | " \n", 3869 | " \n", 3870 | " \n", 3871 | " \n", 3872 | " \n", 3873 | " \n", 3874 | " \n", 3875 | " \n", 3876 | " \n", 3877 | " \n", 3878 | " \n", 3879 | " \n", 3880 | " \n", 3881 | " \n", 3882 | " \n", 3883 | " \n", 3884 | " \n", 3885 | " \n", 3886 | " \n", 3887 | " \n", 3888 | " \n", 3889 | " \n", 3890 | " \n", 3891 | " \n", 3892 | " \n", 3893 | " \n", 3894 | " \n", 3895 | " \n", 3896 | " \n", 3897 | " \n", 3898 | " \n", 3899 | " \n", 3900 | " \n", 3901 | " \n", 3902 | " \n", 3903 | " \n", 3904 | " \n", 3905 | " \n", 3906 | " \n", 3907 | " \n", 3908 | " \n", 3909 | " \n", 3910 | " \n", 3911 | " \n", 3912 | " \n", 3913 | " \n", 3914 | " \n", 3915 | " \n", 3916 | " \n", 3917 | " \n", 3918 | " \n", 3919 | " \n", 3920 | " \n", 3921 | " \n", 3922 | " \n", 3923 | " \n", 3924 | " \n", 3925 | " \n", 3926 | " \n", 3927 | " \n", 3928 | " \n", 3929 | " \n", 3930 | " \n", 3931 | " \n", 3932 | " \n", 3933 | " \n", 3934 | " \n", 3935 | " \n", 3936 | " \n", 3937 | " \n", 3938 | " \n", 3939 | " \n", 3940 | " \n", 3941 | " \n", 3942 | " \n", 3943 | " \n", 3944 | " \n", 3945 | " \n", 3946 | " \n", 3947 | " \n", 3948 | " \n", 3949 | " \n", 3950 | " \n", 3951 | " \n", 3952 | " \n", 3953 | " \n", 3954 | " \n", 3955 | " \n", 3956 | " \n", 3957 | " \n", 3958 | " \n", 3959 | " \n", 3960 | " \n", 3961 | " \n", 3962 | " \n", 3963 | " \n", 3964 | " \n", 3965 | " \n", 3966 | " \n", 3967 | " \n", 3968 | " \n", 3969 | " \n", 3970 | "
show_idtypetitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
329s330MovieCatch Me If You CanSteven SpielbergLeonardo DiCaprio, Tom Hanks, Christopher Walk...United States, CanadaAugust 1, 20212002PG-13142 minDramasAn FBI agent makes it his mission to put cunni...
340s341MovieInceptionChristopher NolanLeonardo DiCaprio, Joseph Gordon-Levitt, Ellio...United States, United KingdomAugust 1, 20212010PG-13148 minAction & Adventure, Sci-Fi & Fantasy, ThrillersA troubled thief who extracts secrets from peo...
392s393MovieDjango UnchainedQuentin TarantinoJamie Foxx, Christoph Waltz, Leonardo DiCaprio...United StatesJuly 24, 20212012R165 minAction & Adventure, DramasAccompanied by a German bounty hunter, a freed...
1358s1359MovieShutter IslandMartin ScorseseLeonardo DiCaprio, Mark Ruffalo, Ben Kingsley,...United StatesFebruary 1, 20212010R139 minThrillersA U.S. marshal's troubling visions compromise ...
1469s1470MovieWhat's Eating Gilbert GrapeLasse HallströmJohnny Depp, Leonardo DiCaprio, Juliette Lewis...United StatesJanuary 1, 20211993PG-13118 minClassic Movies, Dramas, Independent MoviesIn a backwater Iowa town, young Gilbert is tor...
6272s6273MovieBefore the FloodFisher StevensLeonardo DiCaprioUnited StatesFebruary 1, 20182016PG97 minDocumentariesLeonardo DiCaprio crisscrosses the globe to in...
6826s6827MovieGangs of New YorkMartin ScorseseLeonardo DiCaprio, Daniel Day-Lewis, Cameron D...United States, ItalyAugust 20, 20192002R167 minDramasIn the crime-ridden slums of New York in the 1...
7865s7866MovieRevolutionary RoadSam MendesLeonardo DiCaprio, Kate Winslet, Kathy Bates, ...United States, United KingdomNovember 1, 20192008R120 minDramas, Romantic MoviesApril and Frank's marriage unravels when a pla...
8272s8273MovieThe DepartedMartin ScorseseLeonardo DiCaprio, Matt Damon, Jack Nicholson,...United States, Hong KongJanuary 1, 20212006R151 minDramas, ThrillersTwo rookie Boston cops are sent deep undercove...
\n", 3971 | "
" 3972 | ], 3973 | "text/plain": [ 3974 | " show_id type title director \\\n", 3975 | "329 s330 Movie Catch Me If You Can Steven Spielberg \n", 3976 | "340 s341 Movie Inception Christopher Nolan \n", 3977 | "392 s393 Movie Django Unchained Quentin Tarantino \n", 3978 | "1358 s1359 Movie Shutter Island Martin Scorsese \n", 3979 | "1469 s1470 Movie What's Eating Gilbert Grape Lasse Hallström \n", 3980 | "6272 s6273 Movie Before the Flood Fisher Stevens \n", 3981 | "6826 s6827 Movie Gangs of New York Martin Scorsese \n", 3982 | "7865 s7866 Movie Revolutionary Road Sam Mendes \n", 3983 | "8272 s8273 Movie The Departed Martin Scorsese \n", 3984 | "\n", 3985 | " cast \\\n", 3986 | "329 Leonardo DiCaprio, Tom Hanks, Christopher Walk... \n", 3987 | "340 Leonardo DiCaprio, Joseph Gordon-Levitt, Ellio... \n", 3988 | "392 Jamie Foxx, Christoph Waltz, Leonardo DiCaprio... \n", 3989 | "1358 Leonardo DiCaprio, Mark Ruffalo, Ben Kingsley,... \n", 3990 | "1469 Johnny Depp, Leonardo DiCaprio, Juliette Lewis... \n", 3991 | "6272 Leonardo DiCaprio \n", 3992 | "6826 Leonardo DiCaprio, Daniel Day-Lewis, Cameron D... \n", 3993 | "7865 Leonardo DiCaprio, Kate Winslet, Kathy Bates, ... \n", 3994 | "8272 Leonardo DiCaprio, Matt Damon, Jack Nicholson,... \n", 3995 | "\n", 3996 | " country date_added release_year rating \\\n", 3997 | "329 United States, Canada August 1, 2021 2002 PG-13 \n", 3998 | "340 United States, United Kingdom August 1, 2021 2010 PG-13 \n", 3999 | "392 United States July 24, 2021 2012 R \n", 4000 | "1358 United States February 1, 2021 2010 R \n", 4001 | "1469 United States January 1, 2021 1993 PG-13 \n", 4002 | "6272 United States February 1, 2018 2016 PG \n", 4003 | "6826 United States, Italy August 20, 2019 2002 R \n", 4004 | "7865 United States, United Kingdom November 1, 2019 2008 R \n", 4005 | "8272 United States, Hong Kong January 1, 2021 2006 R \n", 4006 | "\n", 4007 | " duration listed_in \\\n", 4008 | "329 142 min Dramas \n", 4009 | "340 148 min Action & Adventure, Sci-Fi & Fantasy, Thrillers \n", 4010 | "392 165 min Action & Adventure, Dramas \n", 4011 | "1358 139 min Thrillers \n", 4012 | "1469 118 min Classic Movies, Dramas, Independent Movies \n", 4013 | "6272 97 min Documentaries \n", 4014 | "6826 167 min Dramas \n", 4015 | "7865 120 min Dramas, Romantic Movies \n", 4016 | "8272 151 min Dramas, Thrillers \n", 4017 | "\n", 4018 | " description \n", 4019 | "329 An FBI agent makes it his mission to put cunni... \n", 4020 | "340 A troubled thief who extracts secrets from peo... \n", 4021 | "392 Accompanied by a German bounty hunter, a freed... \n", 4022 | "1358 A U.S. marshal's troubling visions compromise ... \n", 4023 | "1469 In a backwater Iowa town, young Gilbert is tor... \n", 4024 | "6272 Leonardo DiCaprio crisscrosses the globe to in... \n", 4025 | "6826 In the crime-ridden slums of New York in the 1... \n", 4026 | "7865 April and Frank's marriage unravels when a pla... \n", 4027 | "8272 Two rookie Boston cops are sent deep undercove... " 4028 | ] 4029 | }, 4030 | "execution_count": 196, 4031 | "metadata": {}, 4032 | "output_type": "execute_result" 4033 | } 4034 | ], 4035 | "source": [ 4036 | "# Filter by cast\n", 4037 | "data_not_null[data_not_null['cast'].str.contains('Leonardo DiCaprio')]" 4038 | ] 4039 | }, 4040 | { 4041 | "cell_type": "code", 4042 | "execution_count": 225, 4043 | "id": "f6f3e4c0", 4044 | "metadata": {}, 4045 | "outputs": [ 4046 | { 4047 | "data": { 4048 | "text/html": [ 4049 | "
\n", 4050 | "\n", 4063 | "\n", 4064 | " \n", 4065 | " \n", 4066 | " \n", 4067 | " \n", 4068 | " \n", 4069 | " \n", 4070 | " \n", 4071 | " \n", 4072 | " \n", 4073 | " \n", 4074 | " \n", 4075 | " \n", 4076 | " \n", 4077 | " \n", 4078 | " \n", 4079 | " \n", 4080 | " \n", 4081 | " \n", 4082 | " \n", 4083 | "
typecount
0Movie6131
1TV Show2676
\n", 4084 | "
" 4085 | ], 4086 | "text/plain": [ 4087 | " type count\n", 4088 | "0 Movie 6131\n", 4089 | "1 TV Show 2676" 4090 | ] 4091 | }, 4092 | "execution_count": 225, 4093 | "metadata": {}, 4094 | "output_type": "execute_result" 4095 | } 4096 | ], 4097 | "source": [ 4098 | "df.groupby([\"type\"], as_index=False).agg(count=pd.NamedAgg(column=\"type\", aggfunc=\"count\"))" 4099 | ] 4100 | }, 4101 | { 4102 | "cell_type": "code", 4103 | "execution_count": 226, 4104 | "id": "9ecd94ca", 4105 | "metadata": {}, 4106 | "outputs": [ 4107 | { 4108 | "data": { 4109 | "text/html": [ 4110 | "
\n", 4111 | "\n", 4124 | "\n", 4125 | " \n", 4126 | " \n", 4127 | " \n", 4128 | " \n", 4129 | " \n", 4130 | " \n", 4131 | " \n", 4132 | " \n", 4133 | " \n", 4134 | " \n", 4135 | " \n", 4136 | " \n", 4137 | " \n", 4138 | " \n", 4139 | " \n", 4140 | " \n", 4141 | " \n", 4142 | " \n", 4143 | " \n", 4144 | " \n", 4145 | " \n", 4146 | " \n", 4147 | " \n", 4148 | " \n", 4149 | " \n", 4150 | " \n", 4151 | " \n", 4152 | " \n", 4153 | " \n", 4154 | " \n", 4155 | " \n", 4156 | " \n", 4157 | " \n", 4158 | " \n", 4159 | " \n", 4160 | " \n", 4161 | " \n", 4162 | " \n", 4163 | " \n", 4164 | " \n", 4165 | " \n", 4166 | " \n", 4167 | " \n", 4168 | " \n", 4169 | " \n", 4170 | " \n", 4171 | " \n", 4172 | " \n", 4173 | " \n", 4174 | "
typeshow_idtitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
0Movie61316131594356565691613161316129612861316131
1TV Show2676267623023262285266626762674267626762676
\n", 4175 | "
" 4176 | ], 4177 | "text/plain": [ 4178 | " type show_id title director cast country date_added release_year \\\n", 4179 | "0 Movie 6131 6131 5943 5656 5691 6131 6131 \n", 4180 | "1 TV Show 2676 2676 230 2326 2285 2666 2676 \n", 4181 | "\n", 4182 | " rating duration listed_in description \n", 4183 | "0 6129 6128 6131 6131 \n", 4184 | "1 2674 2676 2676 2676 " 4185 | ] 4186 | }, 4187 | "execution_count": 226, 4188 | "metadata": {}, 4189 | "output_type": "execute_result" 4190 | } 4191 | ], 4192 | "source": [ 4193 | "df.groupby([\"type\"], as_index=False).count()" 4194 | ] 4195 | }, 4196 | { 4197 | "cell_type": "code", 4198 | "execution_count": 227, 4199 | "id": "2f4f41f7", 4200 | "metadata": {}, 4201 | "outputs": [ 4202 | { 4203 | "data": { 4204 | "text/html": [ 4205 | "
\n", 4206 | "\n", 4219 | "\n", 4220 | " \n", 4221 | " \n", 4222 | " \n", 4223 | " \n", 4224 | " \n", 4225 | " \n", 4226 | " \n", 4227 | " \n", 4228 | " \n", 4229 | " \n", 4230 | " \n", 4231 | " \n", 4232 | " \n", 4233 | " \n", 4234 | " \n", 4235 | " \n", 4236 | " \n", 4237 | " \n", 4238 | " \n", 4239 | " \n", 4240 | " \n", 4241 | " \n", 4242 | " \n", 4243 | " \n", 4244 | " \n", 4245 | " \n", 4246 | " \n", 4247 | " \n", 4248 | " \n", 4249 | " \n", 4250 | " \n", 4251 | " \n", 4252 | " \n", 4253 | " \n", 4254 | " \n", 4255 | " \n", 4256 | " \n", 4257 | " \n", 4258 | " \n", 4259 | " \n", 4260 | " \n", 4261 | " \n", 4262 | " \n", 4263 | " \n", 4264 | " \n", 4265 | " \n", 4266 | " \n", 4267 | " \n", 4268 | " \n", 4269 | " \n", 4270 | " \n", 4271 | " \n", 4272 | " \n", 4273 | " \n", 4274 | " \n", 4275 | " \n", 4276 | " \n", 4277 | " \n", 4278 | " \n", 4279 | " \n", 4280 | "
show_idtitledirectorcastcountrydate_addedrelease_yearratingdurationlisted_indescription
type
Movie61316131594356565691613161316129612861316131
TV Show2676267623023262285266626762674267626762676
\n", 4281 | "
" 4282 | ], 4283 | "text/plain": [ 4284 | " show_id title director cast country date_added release_year \\\n", 4285 | "type \n", 4286 | "Movie 6131 6131 5943 5656 5691 6131 6131 \n", 4287 | "TV Show 2676 2676 230 2326 2285 2666 2676 \n", 4288 | "\n", 4289 | " rating duration listed_in description \n", 4290 | "type \n", 4291 | "Movie 6129 6128 6131 6131 \n", 4292 | "TV Show 2674 2676 2676 2676 " 4293 | ] 4294 | }, 4295 | "execution_count": 227, 4296 | "metadata": {}, 4297 | "output_type": "execute_result" 4298 | } 4299 | ], 4300 | "source": [ 4301 | "df.groupby([\"type\"]).count()" 4302 | ] 4303 | }, 4304 | { 4305 | "cell_type": "code", 4306 | "execution_count": null, 4307 | "id": "1e0c6687", 4308 | "metadata": {}, 4309 | "outputs": [], 4310 | "source": [] 4311 | } 4312 | ], 4313 | "metadata": { 4314 | "kernelspec": { 4315 | "display_name": "Python 3", 4316 | "language": "python", 4317 | "name": "python3" 4318 | }, 4319 | "language_info": { 4320 | "codemirror_mode": { 4321 | "name": "ipython", 4322 | "version": 3 4323 | }, 4324 | "file_extension": ".py", 4325 | "mimetype": "text/x-python", 4326 | "name": "python", 4327 | "nbconvert_exporter": "python", 4328 | "pygments_lexer": "ipython3", 4329 | "version": "3.8.8" 4330 | } 4331 | }, 4332 | "nbformat": 4, 4333 | "nbformat_minor": 5 4334 | } 4335 | -------------------------------------------------------------------------------- /pip_packages/pandas/netflix/titles.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lifeparticle/Python-Cheatsheet/79cf6a5d41c92a99c3659dfd487c8e04bc17db9c/pip_packages/pandas/netflix/titles.xlsx -------------------------------------------------------------------------------- /pip_packages/scipy/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /scripts/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /scripts/bitbucket/README.md: -------------------------------------------------------------------------------- 1 | # Resources 2 | 3 | 1. [How to access bitbucket using app password](https://stackoverflow.com/questions/39886995/how-to-access-bitbucket-using-app-password) 4 | -------------------------------------------------------------------------------- /scripts/bitbucket/main.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | from requests.auth import HTTPBasicAuth 4 | 5 | def get_data(): 6 | response = requests.request( 7 | "GET", 8 | url, 9 | auth=HTTPBasicAuth(app_user_name, app_pass) 10 | ) 11 | 12 | return json.loads(response.text) 13 | 14 | url = "https://api.bitbucket.org/2.0/repositories/workspacename" 15 | app_pass = "********" 16 | app_user_name = "**********" 17 | 18 | json_data = get_data() 19 | 20 | repos = [] 21 | 22 | first_pass = True 23 | condition = False 24 | while first_pass or condition: 25 | first_pass = False 26 | for repo in json_data["values"]: 27 | print(repo["full_name"]) 28 | repos.append(repo["full_name"].replace("workspacename/", "")) 29 | if not (json_data.get('next') is None): 30 | url = json_data["next"] 31 | json_data = get_data() 32 | condition = True 33 | else: 34 | condition = False 35 | 36 | print(sorted(repos)) 37 | -------------------------------------------------------------------------------- /scripts/data_vis/.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store -------------------------------------------------------------------------------- /scripts/data_vis/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn main:server -------------------------------------------------------------------------------- /scripts/data_vis/main.py: -------------------------------------------------------------------------------- 1 | import dash 2 | from dash import dcc 3 | from dash import html 4 | import plotly.express as px 5 | import pandas as pd 6 | 7 | app = dash.Dash(__name__) 8 | server = app.server 9 | 10 | df = pd.read_csv("netflix_titles.csv") 11 | df.drop_duplicates(inplace=True) 12 | 13 | pie_fig = px.pie( 14 | data_frame=df, 15 | names='type', 16 | hole=0.8, 17 | title='TV Show vs. Movie') 18 | 19 | bar_fig = px.bar( 20 | data_frame=df.groupby(["type"], as_index=False).agg(count=pd.NamedAgg(column="type", aggfunc="count")), 21 | x='type', 22 | y='count', 23 | color='type', 24 | title='TV Show vs. Movie') 25 | 26 | app.layout = html.Div(children=[ 27 | html.H1(children='Visualizing Netflix Data With Python'), 28 | html.Div(children=''' 29 | Using Pandas, Plotly Express, and Dash. 30 | '''), 31 | html.Div([ 32 | dcc.Graph( 33 | id='graph1', 34 | figure=pie_fig 35 | ), 36 | ]), 37 | html.Div([ 38 | dcc.Graph( 39 | id='graph2', 40 | figure=bar_fig 41 | ), 42 | ]), 43 | ]) 44 | 45 | if __name__ == '__main__': 46 | app.run_server(debug=True) -------------------------------------------------------------------------------- /scripts/data_vis/requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | plotly 3 | dash 4 | gunicorn -------------------------------------------------------------------------------- /scripts/omdbapi/README.md: -------------------------------------------------------------------------------- 1 | ```shell 2 | pip install omdb 3 | ``` 4 | -------------------------------------------------------------------------------- /scripts/omdbapi/main.py: -------------------------------------------------------------------------------- 1 | import omdb 2 | 3 | def movie_info(movie): 4 | try: 5 | a = omdb.get(title=movie[0], year=movie[1]) 6 | print(a["country"]) 7 | except: 8 | print("An exception occurred") 9 | 10 | omdb.set_default('apikey', "5fe52aac") 11 | 12 | movie_list = [ 13 | ["Shutter Island","2010"], 14 | ["Inception","2010"], 15 | ["Gone Girl","2014"] 16 | ] 17 | 18 | for movie in movie_list: 19 | movie_info(movie) -------------------------------------------------------------------------------- /scripts/web_scraper/README.md: -------------------------------------------------------------------------------- 1 | Make sure that your ChromeDriver version and Chrome browser version are the same. For mac you can run the following command 2 | 3 | ```shell 4 | brew upgrade --cask chromedriver 5 | ``` 6 | 7 | This version of ChromeDriver only supports Chrome version 91 8 | Current browser version is 93.0.4577.63 with binary path /Applications/Google Chrome.app/Contents/MacOS/Google Chrome 9 | -------------------------------------------------------------------------------- /scripts/web_scraper/web_scraper.py: -------------------------------------------------------------------------------- 1 | from selenium import webdriver 2 | from urllib.parse import urljoin 3 | from selenium.webdriver.chrome.options import Options 4 | 5 | def navigate(driver, url, page_no): 6 | driver.get(urljoin(url, str(page_no))) 7 | 8 | 9 | def get_data(driver, class_name): 10 | return driver.find_elements_by_class_name(class_name) 11 | 12 | 13 | def has_data(data): 14 | return len(data) > 0 15 | 16 | def parse_data(data, class_names): 17 | for quote in data: 18 | text = quote.find_element_by_class_name(class_names["text"]).text 19 | author = quote.find_element_by_class_name(class_names["author"]).text 20 | print("%s -- %s" %(text, author)) 21 | 22 | def main(): 23 | options = Options() 24 | options.add_argument('--headless') 25 | driver = webdriver.Chrome(options=options) 26 | url = "http://quotes.toscrape.com/page/" 27 | page_no = 1 28 | while True: 29 | navigate(driver, url, page_no) 30 | data = get_data(driver, "quote") 31 | parse_data(data, {"text": "text", "author": "author"}) 32 | page_no += 1 33 | if (has_data(data) == False): 34 | break 35 | driver.close() 36 | 37 | 38 | if __name__ == '__main__': 39 | main() --------------------------------------------------------------------------------