├── .gitignore ├── .nojekyll ├── 1.md ├── 10.md ├── 11.md ├── 2.md ├── 3.md ├── 4.md ├── 404.html ├── 5.md ├── 6.md ├── 7.md ├── 8.md ├── 9.md ├── CNAME ├── Dockerfile ├── LICENSE ├── README.md ├── SUMMARY.md ├── asset ├── docsify-apachecn-footer.js ├── docsify-baidu-push.js ├── docsify-baidu-stat.js ├── docsify-clicker.js ├── docsify-cnzz.js ├── docsify-copy-code.min.js ├── docsify.min.js ├── prism-darcula.css ├── prism-python.min.js ├── search.min.js ├── style.css └── vue.css ├── cover.jpg ├── img ├── 004a97d41149feb93b586776df1f71e7.png ├── 009895845989ad1ea1fcc4395908e79d.png ├── 00d52c954dc9a38ee7c3bf68f3709ed4.png ├── 0184d7263324766479df52da528e189f.png ├── 0254d207b15487baf72decc2d0d18a62.png ├── 038dbeff419cec6d2a15e112b574c134.png ├── 03acf4cd5b2486cba6c1904b42775974.png ├── 0417ca1edade3ed24e1cadd999ded7a0.png ├── 0439b4cfb330ddf02adf058e69fa3b0f.png ├── 047ffd438b3e05e83c5f314f57e91202.png ├── 04ce563b0cb4106e028fd0ad1d5d26d8.png ├── 0569b86eaff787453f1f176c46a3a523.png ├── 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efbeb54fba4ae0a18a99d70fffac3cdc.png ├── f0652d93426a6162a5c71728d56eb569.png ├── f06bfbc572fa81e9899f9978953b9f67.png ├── f1a4430b23300466e6858d7be812ce85.png ├── f24cdd93d12da642e86454e1414c6f00.png ├── f5474d196c6dbefb3c5bc83dc82c1e8f.png ├── f67bd19256dbe2c6ad06a1d65a105ed8.png ├── f7b4633430a35e81b8744bee3128efb6.png ├── f84ac1e15fb30afb633da594087db63b.png ├── fa7e65f9749dc567a2ab2a3443b3b941.png ├── fb17611ffca8afa48fff37a3befabba6.png ├── fb32a5b4eac63f27f95e8f9fdb4ca384.png ├── fb3cbcce83e2ffbf48f7f1cb44c6c1d9.png ├── fbdf210fe62d879637e207c8baed8bbc.png ├── fc94f74a46df56f35afc265b23abf61a.png ├── fceaba48965284f21078a67e4b66e65d.png ├── fd21ad2b106138877782ed37caacc7f3.png ├── fdac9cb61eb80dac7076038bda2eea88.png ├── fde25e23171a044b32c513b99623366f.png ├── ff1131c40bc912add6705ba616a24a63.png ├── ffc1c3d42c8e1739767be73e5ff0f884.png └── ffcddc5b9b5e65c84a1cc5246c721d30.png ├── index.html ├── styles ├── ebook.css └── runoob.css └── update.sh /.gitignore: -------------------------------------------------------------------------------- 1 | _book 2 | Thumbs.db 3 | node_modules -------------------------------------------------------------------------------- /.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/apachecn/pandas-cookbook-code-notes/2841f9bc0f91a9ed544d535d6c16775cc9d49d27/.nojekyll -------------------------------------------------------------------------------- /3.md: -------------------------------------------------------------------------------- 1 | # 第03章 数据分析入门 2 | 3 | 4 | ```py 5 | In[1]: import pandas as pd 6 | import numpy as np 7 | from IPython.display import display 8 | pd.options.display.max_columns = 50 9 | ``` 10 | 11 | ## 1\. 规划数据分析路线 12 | 13 | ```py 14 | # 读取查看数据 15 | In[2]: college = pd.read_csv('data/college.csv') 16 | In[3]: college.head() 17 | Out[3]: 18 | ``` 19 | 20 | ![](img/a8cc827edf04bcfec5b3b0b4460254e8.png) 21 | 22 | ```py 23 | # 数据的行数与列数 24 | In[4]: college.shape 25 | Out[4]: (7535, 27) 26 | ``` 27 | 28 | ```py 29 | # 统计数值列，并进行转置 30 | In[5]: with pd.option_context('display.max_rows', 8): 31 | display(college.describe(include=[np.number]).T) 32 | Out[5]: 33 | ``` 34 | 35 | ![](img/fd21ad2b106138877782ed37caacc7f3.png) 36 | 37 | ```py 38 | # 统计对象和类型列 39 | In[6]: college.describe(include=[np.object, pd.Categorical]).T 40 | Out[6]: 41 | ``` 42 | 43 | ![](img/19d1e25e1911532961c819f376b485ff.png) 44 | 45 | ```py 46 | # 列出每列的数据类型，非缺失值的数量，以及内存的使用 47 | In[7]: college.info() 48 | 49 | RangeIndex: 7535 entries, 0 to 7534 50 | Data columns (total 27 columns): 51 | INSTNM 7535 non-null object 52 | CITY 7535 non-null object 53 | STABBR 7535 non-null object 54 | HBCU 7164 non-null float64 55 | MENONLY 7164 non-null float64 56 | WOMENONLY 7164 non-null float64 57 | RELAFFIL 7535 non-null int64 58 | SATVRMID 1185 non-null float64 59 | SATMTMID 1196 non-null float64 60 | DISTANCEONLY 7164 non-null float64 61 | UGDS 6874 non-null float64 62 | UGDS_WHITE 6874 non-null float64 63 | UGDS_BLACK 6874 non-null float64 64 | UGDS_HISP 6874 non-null float64 65 | UGDS_ASIAN 6874 non-null float64 66 | UGDS_AIAN 6874 non-null float64 67 | UGDS_NHPI 6874 non-null float64 68 | UGDS_2MOR 6874 non-null float64 69 | UGDS_NRA 6874 non-null float64 70 | UGDS_UNKN 6874 non-null float64 71 | PPTUG_EF 6853 non-null float64 72 | CURROPER 7535 non-null int64 73 | PCTPELL 6849 non-null float64 74 | PCTFLOAN 6849 non-null float64 75 | UG25ABV 6718 non-null float64 76 | MD_EARN_WNE_P10 6413 non-null object 77 | GRAD_DEBT_MDN_SUPP 7503 non-null object 78 | dtypes: float64(20), int64(2), object(5) 79 | memory usage: 1.6+ MB 80 | ``` 81 | 82 | ```py 83 | # 重复了，但没设置最大行数 84 | In[8]: college.describe(include=[np.number]).T 85 | Out[8]: 86 | ``` 87 | 88 | ![](img/48e724e011ff09b5ebae56a10fa12166.png) 89 | 90 | ```py 91 | # 和前面重复了 92 | In[9]: college.describe(include=[np.object, pd.Categorical]).T 93 | Out[9]: 94 | ``` 95 | 96 | ![](img/2f8f438f1e9c00be692d2e686091972b.png) 97 | 98 | ### 更多 99 | 100 | ```py 101 | # 在describe方法中，打印分位数 102 | In[10]: with pd.option_context('display.max_rows', 5): 103 | display(college.describe(include=[np.number], 104 | percentiles=[.01, .05, .10, .25, .5, .75, .9, .95, .99]).T) 105 | ``` 106 | 107 | ![](img/c730a91f67b618a47d9eb3d9e155b911.png) 108 | 109 | ```py 110 | # 展示一个数据字典：数据字典的主要作用是解释列名的意义 111 | In[11]: college_dd = pd.read_csv('data/college_data_dictionary.csv') 112 | In[12]: with pd.option_context('display.max_rows', 8): 113 | display(college_dd) 114 | ``` 115 | 116 | ![](img/9fe460c47c08612a7283d50ce2e1d139.png) 117 | 118 | ## 2\. 改变数据类型，降低内存消耗 119 | 120 | ```py 121 | # 选取五列 122 | In[13]: college = pd.read_csv('data/college.csv') 123 | different_cols = ['RELAFFIL', 'SATMTMID', 'CURROPER', 'INSTNM', 'STABBR'] 124 | col2 = college.loc[:, different_cols] 125 | col2.head() 126 | Out[13]: 127 | ``` 128 | 129 | ![](img/ebb158c55755cdab769a251b1b9460a3.png) 130 | 131 | ```py 132 | # 查看数据类型 133 | In[14]: col2.dtypes 134 | Out[14]: RELAFFIL int64 135 | SATMTMID float64 136 | CURROPER int64 137 | INSTNM object 138 | STABBR object 139 | dtype: object 140 | ``` 141 | 142 | ```py 143 | # 用memory_usage方法查看每列的内存消耗 144 | In[15]: original_mem = col2.memory_usage(deep=True) 145 | original_mem 146 | Out[15]: Index 80 147 | RELAFFIL 60280 148 | SATMTMID 60280 149 | CURROPER 60280 150 | INSTNM 660240 151 | STABBR 444565 152 | dtype: int64 153 | ``` 154 | 155 | ```py 156 | # RELAFFIL这列只包含0或1，因此没必要用64位，使用astype方法将其变为8位（1字节）整数 157 | In[16]: col2['RELAFFIL'] = col2['RELAFFIL'].astype(np.int8) 158 | # 再次查看数据类型 159 | In[17]: col2.dtypes 160 | Out[17]: RELAFFIL int8 161 | SATMTMID float64 162 | CURROPER int64 163 | INSTNM object 164 | STABBR object 165 | dtype: object 166 | ``` 167 | 168 | ```py 169 | # 检查两个对象列的独立值的个数 170 | In[18]: col2.select_dtypes(include=['object']).nunique() 171 | Out[18]: INSTNM 7535 172 | STABBR 59 173 | dtype: int64 174 | ``` 175 | 176 | ```py 177 | # STABBR列可以转变为“类型”（Categorical），独立值的个数小于总数的1% 178 | In[19]: col2['STABBR'] = col2['STABBR'].astype('category') 179 | col2.dtypes 180 | Out[19]: RELAFFIL int8 181 | SATMTMID float64 182 | CURROPER int64 183 | INSTNM object 184 | STABBR category 185 | dtype: object 186 | ``` 187 | 188 | ```py 189 | # 再次检查内存的使用 190 | In[20]: new_mem = col2.memory_usage(deep=True) 191 | new_mem 192 | Out[20]: Index 80 193 | RELAFFIL 7535 194 | SATMTMID 60280 195 | CURROPER 60280 196 | INSTNM 660699 197 | STABBR 13576 198 | dtype: int64 199 | ``` 200 | 201 | ```py 202 | # 通过和原始数据比较，RELAFFIL列变为了原来的八分之一，STABBR列只有原始大小的3% 203 | In[21]: new_mem / original_mem 204 | Out[21]: Index 1.000000 205 | RELAFFIL 0.125000 206 | SATMTMID 1.000000 207 | CURROPER 1.000000 208 | INSTNM 1.000695 209 | STABBR 0.030538 210 | dtype: float64 211 | ``` 212 | 213 | ### 更多 214 | 215 | ```py 216 | # CURROPER和INSTNM分别是int64和对象类型 217 | In[22]: college = pd.read_csv('data/college.csv') 218 | In[23]: college[['CURROPER', 'INSTNM']].memory_usage(deep=True) 219 | Out[23]: Index 80 220 | CURROPER 60280 221 | INSTNM 660240 222 | dtype: int64 223 | ``` 224 | 225 | ```py 226 | # CURROPER列加上了10000000，但是内存使用没有变化；但是INSTNM列加上了一个a，内存消耗增加了105字节 227 | In[24]: college.loc[0, 'CURROPER'] = 10000000 228 | college.loc[0, 'INSTNM'] = college.loc[0, 'INSTNM'] + 'a' 229 | # college.loc[1, 'INSTNM'] = college.loc[1, 'INSTNM'] + 'a' 230 | college[['CURROPER', 'INSTNM']].memory_usage(deep=True) 231 | Out[24]: Index 80 232 | CURROPER 60280 233 | INSTNM 660345 234 | dtype: int64 235 | ``` 236 | 237 | ```py 238 | # 数据字典中的信息显示MENONLY这列只包含0和1，但是由于含有缺失值，它的类型是浮点型 239 | In[25]: college['MENONLY'].dtype 240 | Out[25]: dtype('float64') 241 | ``` 242 | 243 | ```py 244 | # 任何数值类型的列，只要有一个缺失值，就会成为浮点型；这列中的任何整数都会强制成为浮点型 245 | In[26]: college['MENONLY'].astype('int8') # ValueError: Cannot convert non-finite values (NA or inf) to integer 246 | --------------------------------------------------------------------------- 247 | ValueError Traceback (most recent call last) 248 | in () 249 | ----> 1 college['MENONLY'].astype('int8') # ValueError: Cannot convert non-finite values (NA or inf) to integer 250 | 251 | ~/anaconda3/lib/python3.6/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs) 252 | 116 else: 253 | 117 kwargs[new_arg_name] = new_arg_value 254 | --> 118 return func(*args, **kwargs) 255 | 119 return wrapper 256 | 120 return _deprecate_kwarg 257 | 258 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs) 259 | 4002 # else, only a single dtype is given 260 | 4003 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors, 261 | -> 4004 **kwargs) 262 | 4005 return self._constructor(new_data).__finalize__(self) 263 | 4006 264 | 265 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, **kwargs) 266 | 3455 267 | 3456 def astype(self, dtype, **kwargs): 268 | -> 3457 return self.apply('astype', dtype=dtype, **kwargs) 269 | 3458 270 | 3459 def convert(self, **kwargs): 271 | 272 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs) 273 | 3322 274 | 3323 kwargs['mgr'] = self 275 | -> 3324 applied = getattr(b, f)(**kwargs) 276 | 3325 result_blocks = _extend_blocks(applied, result_blocks) 277 | 3326 278 | 279 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in astype(self, dtype, copy, errors, values, **kwargs) 280 | 542 def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs): 281 | 543 return self._astype(dtype, copy=copy, errors=errors, values=values, 282 | --> 544 **kwargs) 283 | 545 284 | 546 def _astype(self, dtype, copy=False, errors='raise', values=None, 285 | 286 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py in _astype(self, dtype, copy, errors, values, klass, mgr, **kwargs) 287 | 623 288 | 624 # _astype_nansafe works fine with 1-d only 289 | --> 625 values = astype_nansafe(values.ravel(), dtype, copy=True) 290 | 626 values = values.reshape(self.shape) 291 | 627 292 | 293 | ~/anaconda3/lib/python3.6/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy) 294 | 685 295 | 686 if not np.isfinite(arr).all(): 296 | --> 687 raise ValueError('Cannot convert non-finite values (NA or inf) to ' 297 | 688 'integer') 298 | 689 299 | 300 | ValueError: Cannot convert non-finite values (NA or inf) to integer 301 | ``` 302 | 303 | ```py 304 | # 对于数据类型，可以替换字符串名：27、28、30、31是等价的 305 | In[27]: college.describe(include=['int64', 'float64']).T 306 | Out[27]: 307 | ``` 308 | 309 | ![](img/aa8075118d8dfcf8f979a56ffa28bf4a.png) 310 | 311 | ```py 312 | In[28]: college.describe(include=[np.int64, np.float64]).T 313 | Out[28]: 314 | ``` 315 | 316 | ![](img/8a2bc3a3c23c3372be74556faa1ba358.png) 317 | 318 | ```py 319 | In[29]: college['RELAFFIL'] = college['RELAFFIL'].astype(np.int8) 320 | In[30]: college.describe(include=['int', 'float']).T # defaults to 64 bit int/floats 321 | Out[30]: 322 | ``` 323 | 324 | ![](img/0774e28d28eec4088db64c098bce6f29.png) 325 | 326 | ```py 327 | In[31]: college.describe(include=['number']).T # also works as the default int/float are 64 bits 328 | Out[31]: 329 | ``` 330 | 331 | ![](img/e52a132f12d4ce66721240f8f0439c4c.png) 332 | 333 | ```py 334 | # 转变数据类型时也可以如法炮制 335 | In[32]: college['MENONLY'] = college['MENONLY'].astype('float16') 336 | college['RELAFFIL'] = college['RELAFFIL'].astype('int8') 337 | In[33]: college.index = pd.Int64Index(college.index) 338 | college.index.memory_usage() 339 | Out[33]: 60280 340 | ``` 341 | 342 | ## 3\. 从最大中选择最小 343 | 344 | ```py 345 | # 读取movie.csv，选取'movie_title', 'imdb_score', 'budget'三列 346 | In[34]: movie = pd.read_csv('data/movie.csv') 347 | movie2 = movie[['movie_title', 'imdb_score', 'budget']] 348 | movie2.head() 349 | Out[34]: 350 | ``` 351 | 352 | ![](img/b1d2b182ea5d348df74b7840c52118f4.png) 353 | 354 | ```py 355 | # 用nlargest方法，选出imdb_score分数最高的100个 356 | In[35]: movie2.nlargest(100, 'imdb_score').head() 357 | Out[35]: 358 | ``` 359 | 360 | ![](img/09ea1a77ed48e60eb4e8d44a6595cdc2.png) 361 | 362 | ```py 363 | # 用链式操作，nsmallest方法再从中挑出预算最小的五部 364 | In[36]: movie2.nlargest(100, 'imdb_score').nsmallest(5, 'budget') 365 | Out[36]: 366 | ``` 367 | 368 | ![](img/285d01a7a0f7a6d2b1e0f4b5f717f8f4.png) 369 | 370 | ## 4\. 通过排序选取每组的最大值 371 | 372 | ```py 373 | # 同上，选取出三列。按照title_year降序排列 374 | In[37]: movie = pd.read_csv('data/movie.csv') 375 | movie2 = movie[['movie_title', 'title_year', 'imdb_score']] 376 | In[38]: movie2.sort_values('title_year', ascending=False).head() 377 | Out[38]: 378 | ``` 379 | 380 | ![](img/0184d7263324766479df52da528e189f.png) 381 | 382 | ```py 383 | # 用列表同时对两列进行排序 384 | In[39]: movie3 = movie2.sort_values(['title_year','imdb_score'], ascending=False) 385 | movie3.head() 386 | Out[39]: 387 | ``` 388 | 389 | ![](img/4921878ba8b98994ef66f7228112287c.png) 390 | 391 | ```py 392 | # 用drop_duplicates去重，只保留每年的第一条数据 393 | In[40]: movie_top_year = movie3.drop_duplicates(subset='title_year') 394 | movie_top_year.head() 395 | Out[40]: 396 | ``` 397 | 398 | ![](img/308c242dac870c28d0908b97c01e87e1.png) 399 | 400 | ```py 401 | # 通过给ascending设置列表，可以同时对一列降序排列，一列升序排列 402 | In[41]: movie4 = movie[['movie_title', 'title_year', 'content_rating', 'budget']] 403 | movie4_sorted = movie4.sort_values(['title_year', 'content_rating', 'budget'], 404 | ascending=[False, False, True]) 405 | movie4_sorted.drop_duplicates(subset=['title_year', 'content_rating']).head(10) 406 | Out[41]: 407 | ``` 408 | 409 | ![](img/d0ee4b53c9ed8ecb7c38d8c454373c6f.png) 410 | 411 | ## 5\. 用sort_values复现nlargest方法 412 | 413 | ```py 414 | # 和前面一样nlargest和nsmallest链式操作进行选取 415 | In[42]: movie = pd.read_csv('data/movie.csv') 416 | movie2 = movie[['movie_title', 'imdb_score', 'budget']] 417 | movie_smallest_largest = movie2.nlargest(100, 'imdb_score').nsmallest(5, 'budget') 418 | movie_smallest_largest 419 | Out[42]: 420 | ``` 421 | 422 | ![](img/c9c4d92cdfde1e14de52fb7bfcfbbcc6.png) 423 | 424 | ```py 425 | # 用sort_values方法，选取imdb_score最高的100个 426 | In[43]: movie2.sort_values('imdb_score', ascending=False).head(100).head() 427 | Out[43]: 428 | # 然后可以再.sort_values('budget').head()，选出预算最低的5个，结果如下 429 | ``` 430 | 431 | ![](img/63579a27f5cc87ce385dbe4fea248af2.png) 432 | 433 | 这两种方法得到的最小的5部电影不同，用tail进行调查： 434 | 435 | ```py 436 | # tail可以查看尾部 437 | In[45]: movie2.nlargest(100, 'imdb_score').tail() 438 | Out[45]: 439 | ``` 440 | 441 | ![](img/2810043118e6eccc4327d2f5a4001602.png) 442 | 443 | ```py 444 | In[46]: movie2.sort_values('imdb_score', ascending=False).head(100).tail() 445 | Out[46]: 446 | ``` 447 | 448 | ![](img/34a77fbd6c612f300aa0581249fa8129.png) 449 | 450 | 这是因为评分在8.4以上的电影超过了100部。 451 | 452 | ## 6\. 计算跟踪止损单价格 453 | 454 | ```py 455 | # pip install pandas_datareader 或 conda install pandas_datareader，来安装pandas_datareader 456 | In[47]: import pandas_datareader as pdr 457 | ``` 458 | 459 | > 笔记：pandas_datareader的问题 460 | > pandas_datareader在读取“google”源时会有问题。如果碰到问题，切换到“Yahoo”。 461 | 462 | ```py 463 | # 查询特斯拉在2017年第一天的股价 464 | In[49]: tsla = pdr.DataReader('tsla', data_source='yahoo',start='2017-1-1') 465 | tsla.head(8) 466 | Out[49]: 467 | ``` 468 | 469 | ![](img/488c9ec38eab1443c936207588433c0b.png) 470 | 471 | ```py 472 | # 只关注每天的收盘价，使用cummax得到迄今为止的收盘价最大值 473 | In[50]: tsla_close = tsla['Close'] 474 | In[51]: tsla_cummax = tsla_close.cummax() 475 | tsla_cummax.head(8) 476 | Out[51]: 477 | ``` 478 | 479 | ![](img/7c29ca6cb9cd7820fded21d945df72d3.png) 480 | 481 | ```py 482 | # 将下行区间限制到10%，将tsla_cummax乘以0.9 483 | >>> tsla_trailing_stop = tsla_cummax * .9 484 | >>> tsla_trailing_stop.head(8) 485 | Date 486 | 2017-01-03 195.291 487 | 2017-01-04 204.291 488 | 2017-01-05 204.291 489 | 2017-01-06 206.109 490 | 2017-01-09 208.152 491 | 2017-01-10 208.152 492 | 2017-01-11 208.152 493 | 2017-01-12 208.152 494 | Name: Close, dtype: float64 495 | ``` 496 | 497 | ### 更多 498 | 499 | ```py 500 | # 将上述功能包装成一个函数 501 | In[52]: def set_trailing_loss(symbol, purchase_date, perc): 502 | close = pdr.DataReader(symbol, 'yahoo', start=purchase_date)['Close'] 503 | return close.cummax() * perc 504 | In[53]: tsla_cummax = tsla_close.cummax() 505 | tsla_cummax.head(8) 506 | Out[53]: 507 | ``` 508 | 509 | ![](img/993c74935bc81f0c88b23f854af6da40.png) 510 | 511 | -------------------------------------------------------------------------------- /404.html: -------------------------------------------------------------------------------- 1 | --- 2 | permalink: /404.html 3 | --- 4 | 5 | -------------------------------------------------------------------------------- /CNAME: -------------------------------------------------------------------------------- 1 | pdcb.apachecn.org -------------------------------------------------------------------------------- /Dockerfile: 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Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pandas Cookbook 带注释源码 2 | 3 | ![](cover.jpg) 4 | 5 | > 贡献者：[SeanCheney](https://github.com/iamseancheney) 6 | > 7 | > 软件和教堂非常相似——建成之后我们就在祈祷。——Sam Redwine 8 | 9 | + [ApacheCN 机器学习交流群 629470233](http://shang.qq.com/wpa/qunwpa?idkey=30e5f1123a79867570f665aa3a483ca404b1c3f77737bc01ec520ed5f078ddef) 10 | + [ApacheCN 学习资源](http://www.apachecn.org/) 11 | 12 | ‍ 13 | 14 | + [在线阅读](https://pdcb.apachecn.org) 15 | + [在线阅读（Gitee）](https://apachecn.gitee.io/pandas-cookbook-code-notes/) 16 | + [PDF格式](https://www.gitbook.com/download/pdf/book/wizardforcel/pandas-cookbook-code-notes) 17 | + [EPUB格式](https://www.gitbook.com/download/epub/book/wizardforcel/pandas-cookbook-code-notes) 18 | + [MOBI格式](https://www.gitbook.com/download/mobi/book/wizardforcel/pandas-cookbook-code-notes) 19 | + [代码仓库](https://github.com/apachecn/pandas-cookbook-code-notes) 20 | 21 | 22 | ## 下载 23 | 24 | ### Docker 25 | 26 | ``` 27 | docker pull apachecn0/pandas-cookbook-code-notes 28 | docker run -tid -p :80 apachecn0/pandas-cookbook-code-notes 29 | # 访问 http://localhost:{port} 查看文档 30 | ``` 31 | 32 | ### PYPI 33 | 34 | ``` 35 | pip install pandas-cookbook-code-notes 36 | pandas-cookbook-code-notes 37 | # 访问 http://localhost:{port} 查看文档 38 | ``` 39 | 40 | ### NPM 41 | 42 | ``` 43 | npm install -g pandas-cookbook-code-notes 44 | pandas-cookbook-code-notes 45 | # 访问 http://localhost:{port} 查看文档 46 | ``` 47 | -------------------------------------------------------------------------------- /SUMMARY.md: -------------------------------------------------------------------------------- 1 | + [Pandas Cookbook 带注释源码](README.md) 2 | + [第01章 Pandas基础](1.md) 3 | + [第02章 DataFrame基本操作](2.md) 4 | + [第03章 数据分析入门](3.md) 5 | + [第04章 选取数据子集](4.md) 6 | + [第05章 布尔索引](5.md) 7 | + [第06章 索引对齐](6.md) 8 | + [第07章 分组聚合、过滤、转换](7.md) 9 | + [第08章 数据清理](8.md) 10 | + [第09章 合并Pandas对象](9.md) 11 | + [第10章 时间序列分析](10.md) 12 | + [第11章 用Matplotlib、Pandas、Seaborn进行可视化](11.md) -------------------------------------------------------------------------------- /asset/docsify-apachecn-footer.js: -------------------------------------------------------------------------------- 1 | (function(){ 2 | var footer = [ 3 | '
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21 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | -------------------------------------------------------------------------------- /styles/ebook.css: -------------------------------------------------------------------------------- 1 | /* GitHub stylesheet for MarkdownPad (http://markdownpad.com) */ 2 | /* Author: Nicolas Hery - http://nicolashery.com */ 3 | /* Version: b13fe65ca28d2e568c6ed5d7f06581183df8f2ff */ 4 | /* Source: https://github.com/nicolahery/markdownpad-github */ 5 | 6 | /* RESET 7 | =============================================================================*/ 8 | 9 | html, body, div, span, applet, object, iframe, h1, h2, h3, h4, h5, h6, p, blockquote, pre, a, abbr, acronym, address, big, cite, code, del, dfn, em, img, ins, kbd, q, s, samp, small, strike, strong, sub, sup, tt, var, b, u, i, center, dl, dt, dd, ol, ul, li, fieldset, form, label, legend, table, caption, tbody, tfoot, thead, tr, th, td, article, aside, canvas, details, embed, figure, figcaption, footer, header, hgroup, menu, nav, output, ruby, section, summary, time, mark, audio, video { 10 | margin: 0; 11 | padding: 0; 12 | border: 0; 13 | } 14 | 15 | /* BODY 16 | =============================================================================*/ 17 | 18 | body { 19 | font-family: Helvetica, arial, freesans, clean, sans-serif; 20 | font-size: 14px; 21 | line-height: 1.6; 22 | color: #333; 23 | background-color: #fff; 24 | padding: 20px; 25 | max-width: 960px; 26 | margin: 0 auto; 27 | } 28 | 29 | body>*:first-child { 30 | margin-top: 0 !important; 31 | } 32 | 33 | body>*:last-child { 34 | margin-bottom: 0 !important; 35 | } 36 | 37 | /* BLOCKS 38 | =============================================================================*/ 39 | 40 | p, blockquote, ul, ol, dl, table, pre { 41 | margin: 15px 0; 42 | } 43 | 44 | /* HEADERS 45 | =============================================================================*/ 46 | 47 | h1, h2, h3, h4, h5, h6 { 48 | margin: 20px 0 10px; 49 | padding: 0; 50 | font-weight: bold; 51 | -webkit-font-smoothing: antialiased; 52 | } 53 | 54 | h1 tt, h1 code, h2 tt, h2 code, h3 tt, h3 code, h4 tt, h4 code, h5 tt, h5 code, h6 tt, h6 code { 55 | font-size: inherit; 56 | } 57 | 58 | h1 { 59 | font-size: 24px; 60 | border-bottom: 1px solid #ccc; 61 | color: #000; 62 | } 63 | 64 | h2 { 65 | font-size: 18px; 66 | color: #000; 67 | } 68 | 69 | h3 { 70 | font-size: 14px; 71 | } 72 | 73 | h4 { 74 | font-size: 14px; 75 | } 76 | 77 | h5 { 78 | font-size: 14px; 79 | } 80 | 81 | h6 { 82 | color: #777; 83 | font-size: 14px; 84 | } 85 | 86 | body>h2:first-child, body>h1:first-child, body>h1:first-child+h2, body>h3:first-child, body>h4:first-child, body>h5:first-child, body>h6:first-child { 87 | margin-top: 0; 88 | padding-top: 0; 89 | } 90 | 91 | a:first-child h1, a:first-child h2, a:first-child h3, a:first-child h4, a:first-child h5, a:first-child h6 { 92 | margin-top: 0; 93 | padding-top: 0; 94 | } 95 | 96 | h1+p, h2+p, h3+p, h4+p, h5+p, h6+p { 97 | margin-top: 10px; 98 | } 99 | 100 | /* LINKS 101 | =============================================================================*/ 102 | 103 | a { 104 | color: #4183C4; 105 | text-decoration: none; 106 | } 107 | 108 | a:hover { 109 | text-decoration: underline; 110 | } 111 | 112 | /* LISTS 113 | =============================================================================*/ 114 | 115 | ul, ol { 116 | padding-left: 30px; 117 | } 118 | 119 | ul li > :first-child, 120 | ol li > :first-child, 121 | ul li ul:first-of-type, 122 | ol li ol:first-of-type, 123 | ul li ol:first-of-type, 124 | ol li ul:first-of-type { 125 | margin-top: 0px; 126 | } 127 | 128 | ul ul, ul ol, ol ol, ol ul { 129 | margin-bottom: 0; 130 | } 131 | 132 | dl { 133 | padding: 0; 134 | } 135 | 136 | dl dt { 137 | font-size: 14px; 138 | font-weight: bold; 139 | font-style: italic; 140 | padding: 0; 141 | margin: 15px 0 5px; 142 | } 143 | 144 | dl dt:first-child { 145 | padding: 0; 146 | } 147 | 148 | dl dt>:first-child { 149 | margin-top: 0px; 150 | } 151 | 152 | dl dt>:last-child { 153 | margin-bottom: 0px; 154 | } 155 | 156 | dl dd { 157 | margin: 0 0 15px; 158 | padding: 0 15px; 159 | } 160 | 161 | dl dd>:first-child { 162 | margin-top: 0px; 163 | } 164 | 165 | dl dd>:last-child { 166 | margin-bottom: 0px; 167 | } 168 | 169 | /* CODE 170 | =============================================================================*/ 171 | 172 | pre, code, tt { 173 | font-size: 12px; 174 | font-family: Consolas, "Liberation Mono", Courier, monospace; 175 | } 176 | 177 | code, tt { 178 | margin: 0 0px; 179 | padding: 0px 0px; 180 | white-space: nowrap; 181 | border: 1px solid #eaeaea; 182 | background-color: #f8f8f8; 183 | border-radius: 3px; 184 | } 185 | 186 | pre>code { 187 | margin: 0; 188 | padding: 0; 189 | white-space: pre; 190 | border: none; 191 | background: transparent; 192 | } 193 | 194 | pre { 195 | background-color: #f8f8f8; 196 | border: 1px solid #ccc; 197 | font-size: 13px; 198 | line-height: 19px; 199 | overflow: auto; 200 | padding: 6px 10px; 201 | border-radius: 3px; 202 | } 203 | 204 | pre code, pre tt { 205 | background-color: transparent; 206 | border: none; 207 | } 208 | 209 | kbd { 210 | -moz-border-bottom-colors: none; 211 | -moz-border-left-colors: none; 212 | -moz-border-right-colors: none; 213 | -moz-border-top-colors: none; 214 | background-color: #DDDDDD; 215 | background-image: linear-gradient(#F1F1F1, #DDDDDD); 216 | background-repeat: repeat-x; 217 | border-color: #DDDDDD #CCCCCC #CCCCCC #DDDDDD; 218 | border-image: none; 219 | border-radius: 2px 2px 2px 2px; 220 | border-style: solid; 221 | border-width: 1px; 222 | font-family: "Helvetica Neue",Helvetica,Arial,sans-serif; 223 | line-height: 10px; 224 | padding: 1px 4px; 225 | } 226 | 227 | /* QUOTES 228 | =============================================================================*/ 229 | 230 | blockquote { 231 | border-left: 4px solid #DDD; 232 | padding: 0 15px; 233 | color: #777; 234 | } 235 | 236 | blockquote>:first-child { 237 | margin-top: 0px; 238 | } 239 | 240 | blockquote>:last-child { 241 | margin-bottom: 0px; 242 | } 243 | 244 | /* HORIZONTAL RULES 245 | =============================================================================*/ 246 | 247 | hr { 248 | clear: both; 249 | margin: 15px 0; 250 | height: 0px; 251 | overflow: hidden; 252 | border: none; 253 | background: transparent; 254 | border-bottom: 4px solid #ddd; 255 | padding: 0; 256 | } 257 | 258 | /* TABLES 259 | =============================================================================*/ 260 | 261 | table th { 262 | font-weight: bold; 263 | } 264 | 265 | table th, table td { 266 | border: 1px solid #ccc; 267 | padding: 6px 13px; 268 | } 269 | 270 | table tr { 271 | border-top: 1px solid #ccc; 272 | background-color: #fff; 273 | } 274 | 275 | table tr:nth-child(2n) { 276 | background-color: #f8f8f8; 277 | } 278 | 279 | /* IMAGES 280 | =============================================================================*/ 281 | 282 | img { 283 | max-width: 100% 284 | } -------------------------------------------------------------------------------- /styles/runoob.css: -------------------------------------------------------------------------------- 1 | .example_code { 2 | font-size: 12px; 3 | font-family: Consolas, "Liberation Mono", Courier, monospace; 4 | background-color: #f8f8f8; 5 | border: 1px solid #ccc; 6 | font-size: 13px; 7 | line-height: 19px; 8 | overflow: auto; 9 | padding: 6px 10px; 10 | border-radius: 3px; 11 | white-space: pre-wrap; 12 | } 13 | 14 | .example_code>code { 15 | margin: 0; 16 | padding: 0; 17 | white-space: pre; 18 | border: none; 19 | background: transparent; 20 | } 21 | 22 | .example_code code, .example_code tt { 23 | background-color: transparent; 24 | border: none; 25 | } 26 | 27 | .tryitbtn { 28 | display: none; 29 | } -------------------------------------------------------------------------------- /update.sh: -------------------------------------------------------------------------------- 1 | git add -A 2 | git commit -am "$(date "+%Y-%m-%d %H:%M:%S")" 3 | git push --------------------------------------------------------------------------------