1 第一章：數(shù)據(jù)載入及初步觀察
1.1 載入數(shù)據(jù)
1.1.1 任務(wù)一：導(dǎo)入numpy和pandas

import numpy as np import pandas as pd

1.1.2 任務(wù)二：載入數(shù)據(jù)
(1) 使用相對路徑載入數(shù)據(jù)

df = pd.read_csv("train.csv")

(2) 使用絕對路徑載入數(shù)據(jù)

df = pd.read_csv(r"C:\Users\Administrator\Desktop\數(shù)據(jù)分析/train.csv")

【提示】相對路徑載入報錯時，嘗試使用os.getcwd()查看當(dāng)前工作目錄。
【思考】pd.read_csv()和pd.read_table()的不同

df = pd.read_csv("train.csv") print(df.values) df.values.shape [[1 0 3 ... 7.25 nan 'S'][2 1 1 ... 71.2833 'C85' 'C'][3 1 3 ... 7.925 nan 'S']...[889 0 3 ... 23.45 nan 'S'][890 1 1 ... 30.0 'C148' 'C'][891 0 3 ... 7.75 nan 'Q']] (891, 12) df = pd.read_table("train.csv") df.values.shape

(891, 1)
如上所示，首先兩者的默認(rèn)分隔符不同其次兩者分割的值和方向不同.通過上述例子可以看到read_csv讀取時每一個字符串都作為一列，而read_table讀取時把整體字符串作為一列

【總結(jié)】加載的數(shù)據(jù)是所有工作的第一步，我們的工作會接觸到不同的數(shù)據(jù)格式（eg:.csv;.tsv;.xlsx）,但是加載的方法和思路都是一樣的

1.1.3 任務(wù)三：每1000行為一個數(shù)據(jù)模塊，逐塊讀取

df = pd.read_csv("train.csv", chunksize=1000) df = pd.read_csv("train.csv", chunksize=500) for temp in df:print(temp) PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 0 3 5 6 0 3 6 7 0 1 7 8 0 3 8 9 1 3 9 10 1 2 10 11 1 3 11 12 1 1 12 13 0 3 13 14 0 3 14 15 0 3 15 16 1 2 16 17 0 3 17 18 1 2 18 19 0 3 19 20 1 3 20 21 0 2 21 22 1 2 22 23 1 3 23 24 1 1 24 25 0 3 25 26 1 3 26 27 0 3 27 28 0 1 28 29 1 3 29 30 0 3 .. ... ... ... 470 471 0 3 471 472 0 3 472 473 1 2 473 474 1 2 474 475 0 3 475 476 0 1 476 477 0 2 477 478 0 3 478 479 0 3 479 480 1 3 480 481 0 3 481 482 0 2 482 483 0 3 483 484 1 3 484 485 1 1 485 486 0 3 486 487 1 1 487 488 0 1 488 489 0 3 489 490 1 3 490 491 0 3 491 492 0 3 492 493 0 1 493 494 0 1 494 495 0 3 495 496 0 3 496 497 1 1 497 498 0 3 498 499 0 1 499 500 0 3 Name Sex Age SibSp \ 0 Braund, Mr. Owen Harris male 22.0 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 2 Heikkinen, Miss. Laina female 26.0 0 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 4 Allen, Mr. William Henry male 35.0 0 5 Moran, Mr. James male NaN 0 6 McCarthy, Mr. Timothy J male 54.0 0 7 Palsson, Master. Gosta Leonard male 2.0 3 8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 10 Sandstrom, Miss. Marguerite Rut female 4.0 1 11 Bonnell, Miss. Elizabeth female 58.0 0 12 Saundercock, Mr. William Henry male 20.0 0 13 Andersson, Mr. Anders Johan male 39.0 1 14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 16 Rice, Master. Eugene male 2.0 4 17 Williams, Mr. Charles Eugene male NaN 0 18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 19 Masselmani, Mrs. Fatima female NaN 0 20 Fynney, Mr. Joseph J male 35.0 0 21 Beesley, Mr. Lawrence male 34.0 0 22 McGowan, Miss. Anna "Annie" female 15.0 0 23 Sloper, Mr. William Thompson male 28.0 0 24 Palsson, Miss. Torborg Danira female 8.0 3 25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 26 Emir, Mr. Farred Chehab male NaN 0 27 Fortune, Mr. Charles Alexander male 19.0 3 28 O'Dwyer, Miss. Ellen "Nellie" female NaN 0 29 Todoroff, Mr. Lalio male NaN 0 .. ... ... ... ... 470 Keefe, Mr. Arthur male NaN 0 471 Cacic, Mr. Luka male 38.0 0 472 West, Mrs. Edwy Arthur (Ada Mary Worth) female 33.0 1 473 Jerwan, Mrs. Amin S (Marie Marthe Thuillard) female 23.0 0 474 Strandberg, Miss. Ida Sofia female 22.0 0 475 Clifford, Mr. George Quincy male NaN 0 476 Renouf, Mr. Peter Henry male 34.0 1 477 Braund, Mr. Lewis Richard male 29.0 1 478 Karlsson, Mr. Nils August male 22.0 0 479 Hirvonen, Miss. Hildur E female 2.0 0 480 Goodwin, Master. Harold Victor male 9.0 5 481 Frost, Mr. Anthony Wood "Archie" male NaN 0 482 Rouse, Mr. Richard Henry male 50.0 0 483 Turkula, Mrs. (Hedwig) female 63.0 0 484 Bishop, Mr. Dickinson H male 25.0 1 485 Lefebre, Miss. Jeannie female NaN 3 486 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35.0 1 487 Kent, Mr. Edward Austin male 58.0 0 488 Somerton, Mr. Francis William male 30.0 0 489 Coutts, Master. Eden Leslie "Neville" male 9.0 1 490 Hagland, Mr. Konrad Mathias Reiersen male NaN 1 491 Windelov, Mr. Einar male 21.0 0 492 Molson, Mr. Harry Markland male 55.0 0 493 Artagaveytia, Mr. Ramon male 71.0 0 494 Stanley, Mr. Edward Roland male 21.0 0 495 Yousseff, Mr. Gerious male NaN 0 496 Eustis, Miss. Elizabeth Mussey female 54.0 1 497 Shellard, Mr. Frederick William male NaN 0 498 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0 1 499 Svensson, Mr. Olof male 24.0 0 Parch Ticket Fare Cabin Embarked 0 0 A/5 21171 7.2500 NaN S 1 0 PC 17599 71.2833 C85 C 2 0 STON/O2. 3101282 7.9250 NaN S 3 0 113803 53.1000 C123 S 4 0 373450 8.0500 NaN S 5 0 330877 8.4583 NaN Q 6 0 17463 51.8625 E46 S 7 1 349909 21.0750 NaN S 8 2 347742 11.1333 NaN S 9 0 237736 30.0708 NaN C 10 1 PP 9549 16.7000 G6 S 11 0 113783 26.5500 C103 S 12 0 A/5. 2151 8.0500 NaN S 13 5 347082 31.2750 NaN S 14 0 350406 7.8542 NaN S 15 0 248706 16.0000 NaN S 16 1 382652 29.1250 NaN Q 17 0 244373 13.0000 NaN S 18 0 345763 18.0000 NaN S 19 0 2649 7.2250 NaN C 20 0 239865 26.0000 NaN S 21 0 248698 13.0000 D56 S 22 0 330923 8.0292 NaN Q 23 0 113788 35.5000 A6 S 24 1 349909 21.0750 NaN S 25 5 347077 31.3875 NaN S 26 0 2631 7.2250 NaN C 27 2 19950 263.0000 C23 C25 C27 S 28 0 330959 7.8792 NaN Q 29 0 349216 7.8958 NaN S .. ... ... ... ... ... 470 0 323592 7.2500 NaN S 471 0 315089 8.6625 NaN S 472 2 C.A. 34651 27.7500 NaN S 473 0 SC/AH Basle 541 13.7917 D C 474 0 7553 9.8375 NaN S 475 0 110465 52.0000 A14 S 476 0 31027 21.0000 NaN S 477 0 3460 7.0458 NaN S 478 0 350060 7.5208 NaN S 479 1 3101298 12.2875 NaN S 480 2 CA 2144 46.9000 NaN S 481 0 239854 0.0000 NaN S 482 0 A/5 3594 8.0500 NaN S 483 0 4134 9.5875 NaN S 484 0 11967 91.0792 B49 C 485 1 4133 25.4667 NaN S 486 0 19943 90.0000 C93 S 487 0 11771 29.7000 B37 C 488 0 A.5. 18509 8.0500 NaN S 489 1 C.A. 37671 15.9000 NaN S 490 0 65304 19.9667 NaN S 491 0 SOTON/OQ 3101317 7.2500 NaN S 492 0 113787 30.5000 C30 S 493 0 PC 17609 49.5042 NaN C 494 0 A/4 45380 8.0500 NaN S 495 0 2627 14.4583 NaN C 496 0 36947 78.2667 D20 C 497 0 C.A. 6212 15.1000 NaN S 498 2 113781 151.5500 C22 C26 S 499 0 350035 7.7958 NaN S [500 rows x 12 columns]PassengerId Survived Pclass \ 500 501 0 3 501 502 0 3 502 503 0 3 503 504 0 3 504 505 1 1 505 506 0 1 506 507 1 2 507 508 1 1 508 509 0 3 509 510 1 3 510 511 1 3 511 512 0 3 512 513 1 1 513 514 1 1 514 515 0 3 515 516 0 1 516 517 1 2 517 518 0 3 518 519 1 2 519 520 0 3 520 521 1 1 521 522 0 3 522 523 0 3 523 524 1 1 524 525 0 3 525 526 0 3 526 527 1 2 527 528 0 1 528 529 0 3 529 530 0 2 .. ... ... ... 861 862 0 2 862 863 1 1 863 864 0 3 864 865 0 2 865 866 1 2 866 867 1 2 867 868 0 1 868 869 0 3 869 870 1 3 870 871 0 3 871 872 1 1 872 873 0 1 873 874 0 3 874 875 1 2 875 876 1 3 876 877 0 3 877 878 0 3 878 879 0 3 879 880 1 1 880 881 1 2 881 882 0 3 882 883 0 3 883 884 0 2 884 885 0 3 885 886 0 3 886 887 0 2 887 888 1 1 888 889 0 3 889 890 1 1 890 891 0 3 Name Sex Age SibSp \ 500 Calic, Mr. Petar male 17.0 0 501 Canavan, Miss. Mary female 21.0 0 502 O'Sullivan, Miss. Bridget Mary female NaN 0 503 Laitinen, Miss. Kristina Sofia female 37.0 0 504 Maioni, Miss. Roberta female 16.0 0 505 Penasco y Castellana, Mr. Victor de Satode male 18.0 1 506 Quick, Mrs. Frederick Charles (Jane Richards) female 33.0 0 507 Bradley, Mr. George ("George Arthur Brayton") male NaN 0 508 Olsen, Mr. Henry Margido male 28.0 0 509 Lang, Mr. Fang male 26.0 0 510 Daly, Mr. Eugene Patrick male 29.0 0 511 Webber, Mr. James male NaN 0 512 McGough, Mr. James Robert male 36.0 0 513 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54.0 1 514 Coleff, Mr. Satio male 24.0 0 515 Walker, Mr. William Anderson male 47.0 0 516 Lemore, Mrs. (Amelia Milley) female 34.0 0 517 Ryan, Mr. Patrick male NaN 0 518 Angle, Mrs. William A (Florence "Mary" Agnes H... female 36.0 1 519 Pavlovic, Mr. Stefo male 32.0 0 520 Perreault, Miss. Anne female 30.0 0 521 Vovk, Mr. Janko male 22.0 0 522 Lahoud, Mr. Sarkis male NaN 0 523 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44.0 0 524 Kassem, Mr. Fared male NaN 0 525 Farrell, Mr. James male 40.5 0 526 Ridsdale, Miss. Lucy female 50.0 0 527 Farthing, Mr. John male NaN 0 528 Salonen, Mr. Johan Werner male 39.0 0 529 Hocking, Mr. Richard George male 23.0 2 .. ... ... ... ... 861 Giles, Mr. Frederick Edward male 21.0 1 862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 863 Sage, Miss. Dorothy Edith "Dolly" female NaN 8 864 Gill, Mr. John William male 24.0 0 865 Bystrom, Mrs. (Karolina) female 42.0 0 866 Duran y More, Miss. Asuncion female 27.0 1 867 Roebling, Mr. Washington Augustus II male 31.0 0 868 van Melkebeke, Mr. Philemon male NaN 0 869 Johnson, Master. Harold Theodor male 4.0 1 870 Balkic, Mr. Cerin male 26.0 0 871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 872 Carlsson, Mr. Frans Olof male 33.0 0 873 Vander Cruyssen, Mr. Victor male 47.0 0 874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 875 Najib, Miss. Adele Kiamie "Jane" female 15.0 0 876 Gustafsson, Mr. Alfred Ossian male 20.0 0 877 Petroff, Mr. Nedelio male 19.0 0 878 Laleff, Mr. Kristo male NaN 0 879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 881 Markun, Mr. Johann male 33.0 0 882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 883 Banfield, Mr. Frederick James male 28.0 0 884 Sutehall, Mr. Henry Jr male 25.0 0 885 Rice, Mrs. William (Margaret Norton) female 39.0 0 886 Montvila, Rev. Juozas male 27.0 0 887 Graham, Miss. Margaret Edith female 19.0 0 888 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 889 Behr, Mr. Karl Howell male 26.0 0 890 Dooley, Mr. Patrick male 32.0 0 Parch Ticket Fare Cabin Embarked 500 0 315086 8.6625 NaN S 501 0 364846 7.7500 NaN Q 502 0 330909 7.6292 NaN Q 503 0 4135 9.5875 NaN S 504 0 110152 86.5000 B79 S 505 0 PC 17758 108.9000 C65 C 506 2 26360 26.0000 NaN S 507 0 111427 26.5500 NaN S 508 0 C 4001 22.5250 NaN S 509 0 1601 56.4958 NaN S 510 0 382651 7.7500 NaN Q 511 0 SOTON/OQ 3101316 8.0500 NaN S 512 0 PC 17473 26.2875 E25 S 513 0 PC 17603 59.4000 NaN C 514 0 349209 7.4958 NaN S 515 0 36967 34.0208 D46 S 516 0 C.A. 34260 10.5000 F33 S 517 0 371110 24.1500 NaN Q 518 0 226875 26.0000 NaN S 519 0 349242 7.8958 NaN S 520 0 12749 93.5000 B73 S 521 0 349252 7.8958 NaN S 522 0 2624 7.2250 NaN C 523 1 111361 57.9792 B18 C 524 0 2700 7.2292 NaN C 525 0 367232 7.7500 NaN Q 526 0 W./C. 14258 10.5000 NaN S 527 0 PC 17483 221.7792 C95 S 528 0 3101296 7.9250 NaN S 529 1 29104 11.5000 NaN S .. ... ... ... ... ... 861 0 28134 11.5000 NaN S 862 0 17466 25.9292 D17 S 863 2 CA. 2343 69.5500 NaN S 864 0 233866 13.0000 NaN S 865 0 236852 13.0000 NaN S 866 0 SC/PARIS 2149 13.8583 NaN C 867 0 PC 17590 50.4958 A24 S 868 0 345777 9.5000 NaN S 869 1 347742 11.1333 NaN S 870 0 349248 7.8958 NaN S 871 1 11751 52.5542 D35 S 872 0 695 5.0000 B51 B53 B55 S 873 0 345765 9.0000 NaN S 874 0 P/PP 3381 24.0000 NaN C 875 0 2667 7.2250 NaN C 876 0 7534 9.8458 NaN S 877 0 349212 7.8958 NaN S 878 0 349217 7.8958 NaN S 879 1 11767 83.1583 C50 C 880 1 230433 26.0000 NaN S 881 0 349257 7.8958 NaN S 882 0 7552 10.5167 NaN S 883 0 C.A./SOTON 34068 10.5000 NaN S 884 0 SOTON/OQ 392076 7.0500 NaN S 885 5 382652 29.1250 NaN Q 886 0 211536 13.0000 NaN S 887 0 112053 30.0000 B42 S 888 2 W./C. 6607 23.4500 NaN S 889 0 111369 30.0000 C148 C 890 0 370376 7.7500 NaN Q [391 rows x 12 columns]

【思考】什么是逐塊讀取？為什么要逐塊讀取呢？
通過將數(shù)據(jù)集劃分，按塊讀取數(shù)據(jù)集
read_csv中的chunksize參數(shù)設(shè)置分塊大小,返回的是可迭代對象

逐塊讀取原因:

1.數(shù)據(jù)集較大,完全讀取不易看到樣貌
2.讀取時間消耗大，占用內(nèi)存大
3.簡單讀取遇到MemoryError

1.1.4 任務(wù)四：將表頭改成中文，索引改為乘客ID [對于某些英文資料，我們可以通過翻譯來更直觀的熟悉我們的數(shù)據(jù)]
PassengerId => 乘客ID
Survived => 是否幸存
Pclass => 乘客等級(1/2/3等艙位)
Name => 乘客姓名
Sex => 性別
Age => 年齡
SibSp => 堂兄弟/妹個數(shù)
Parch => 父母與小孩個數(shù)
Ticket => 船票信息
Fare => 票價
Cabin => 客艙
Embarked => 登船港口

titles = ["乘客ID", "是否幸存", "乘客等級(1/2/3等艙位)", "乘客姓名", "性別", "年齡", "堂兄弟/妹個數(shù)", "父母與小孩個數(shù)", "船票信息", "票價", "客艙", "登船港口"] index_name = "乘客ID" df = pd.read_csv("train.csv") df.columns = titles df = df.set_index("乘客ID") df.head()

1.2 初步觀察
1.2.1 任務(wù)一：查看數(shù)據(jù)的基本信息

df.info(): # 打印摘要 df.describe(): # 描述性統(tǒng)計信息 df.values: # 數(shù)據(jù) df.shape: # 形狀 (行數(shù), 列數(shù)) df.columns: # 列標(biāo)簽 df.columns.values: # 列標(biāo)簽 df.index: # 行標(biāo)簽 df.index.values: # 行標(biāo)簽 df.head(n): # 前n行 df.tail(n): # 尾n行 pd.options.display.max_columns=n: # 最多顯示n列 pd.options.display.max_rows=n: # 最多顯示n行 df.memory_usage(): # 占用內(nèi)存(字節(jié)B)

1.2.2 任務(wù)二：觀察表格前10行的數(shù)據(jù)和后15行的數(shù)據(jù)

#前十行的乘客 df.head(10)

#后15行的乘客 df.tail(15)

1.2.4 任務(wù)三：判斷數(shù)據(jù)是否為空，為空的地方返回True，其余地方返回False

df.isna() df.isna().sum() df[df.notna().all(1)] 是否幸存 乘客等級(1/2/3等艙位) 乘客姓名 性別 年齡 堂兄弟/妹個數(shù) 父母與小孩個數(shù) 船票信息 票價 客艙 登船港口 乘客ID 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S 22 1 2 Beesley, Mr. Lawrence male 34.0 0 0 248698 13.0000 D56 S 24 1 1 Sloper, Mr. William Thompson male 28.0 0 0 113788 35.5000 A6 S 28 0 1 Fortune, Mr. Charles Alexander male 19.0 3 2 19950 263.0000 C23 C25 C27 S 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0 1 0 PC 17572 76.7292 D33 C 55 0 1 Ostby, Mr. Engelhart Cornelius male 65.0 0 1 113509 61.9792 B30 C 63 0 1 Harris, Mr. Henry Birkhardt male 45.0 1 0 36973 83.4750 C83 S 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.0 0 0 C.A. 29395 10.5000 F33 S 76 0 3 Moen, Mr. Sigurd Hansen male 25.0 0 0 348123 7.6500 F G73 S 89 1 1 Fortune, Miss. Mabel Helen female 23.0 3 2 19950 263.0000 C23 C25 C27 S 93 0 1 Chaffee, Mr. Herbert Fuller male 46.0 1 0 W.E.P. 5734 61.1750 E31 S 97 0 1 Goldschmidt, Mr. George B male 71.0 0 0 PC 17754 34.6542 A5 C 98 1 1 Greenfield, Mr. William Bertram male 23.0 0 1 PC 17759 63.3583 D10 D12 C 103 0 1 White, Mr. Richard Frasar male 21.0 0 1 35281 77.2875 D26 S 111 0 1 Porter, Mr. Walter Chamberlain male 47.0 0 0 110465 52.0000 C110 S 119 0 1 Baxter, Mr. Quigg Edmond male 24.0 0 1 PC 17558 247.5208 B58 B60 C 124 1 2 Webber, Miss. Susan female 32.5 0 0 27267 13.0000 E101 S 125 0 1 White, Mr. Percival Wayland male 54.0 0 1 35281 77.2875 D26 S 137 1 1 Newsom, Miss. Helen Monypeny female 19.0 0 2 11752 26.2833 D47 S 138 0 1 Futrelle, Mr. Jacques Heath male 37.0 1 0 113803 53.1000 C123 S 140 0 1 Giglio, Mr. Victor male 24.0 0 0 PC 17593 79.2000 B86 C 149 0 2 Navratil, Mr. Michel ("Louis M Hoffman") male 36.5 0 2 230080 26.0000 F2 S 152 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22.0 1 0 113776 66.6000 C2 S 171 0 1 Van der hoef, Mr. Wyckoff male 61.0 0 0 111240 33.5000 B19 S 175 0 1 Smith, Mr. James Clinch male 56.0 0 0 17764 30.6958 A7 C 178 0 1 Isham, Miss. Ann Elizabeth female 50.0 0 0 PC 17595 28.7125 C49 C ... ... ... ... ... ... ... ... ... ... ... ... 738 1 1 Lesurer, Mr. Gustave J male 35.0 0 0 PC 17755 512.3292 B101 C 742 0 1 Cavendish, Mr. Tyrell William male 36.0 1 0 19877 78.8500 C46 S 743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21.0 2 2 PC 17608 262.3750 B57 B59 B63 B66 C 746 0 1 Crosby, Capt. Edward Gifford male 70.0 1 1 WE/P 5735 71.0000 B22 S 749 0 1 Marvin, Mr. Daniel Warner male 19.0 1 0 113773 53.1000 D30 S 752 1 3 Moor, Master. Meier male 6.0 0 1 392096 12.4750 E121 S 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dye... female 33.0 0 0 110152 86.5000 B77 S 764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36.0 1 2 113760 120.0000 B96 B98 S 766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51.0 1 0 13502 77.9583 D11 S 773 0 2 Mack, Mrs. (Mary) female 57.0 0 0 S.O./P.P. 3 10.5000 E77 S 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton Mc... female 43.0 0 1 24160 211.3375 B3 S 782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17.0 1 0 17474 57.0000 B20 S 783 0 1 Long, Mr. Milton Clyde male 29.0 0 0 113501 30.0000 D6 S 790 0 1 Guggenheim, Mr. Benjamin male 46.0 0 0 PC 17593 79.2000 B82 B84 C 797 1 1 Leader, Dr. Alice (Farnham) female 49.0 0 0 17465 25.9292 D17 S 803 1 1 Carter, Master. William Thornton II male 11.0 1 2 113760 120.0000 B96 B98 S 807 0 1 Andrews, Mr. Thomas Jr male 39.0 0 0 112050 0.0000 A36 S 810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33.0 1 0 113806 53.1000 E8 S 821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gr... female 52.0 1 1 12749 93.5000 B69 S 824 1 3 Moor, Mrs. (Beila) female 27.0 0 1 392096 12.4750 E121 S 836 1 1 Compton, Miss. Sara Rebecca female 39.0 1 1 PC 17756 83.1583 E49 C 854 1 1 Lines, Miss. Mary Conover female 16.0 0 1 PC 17592 39.4000 D28 S 858 1 1 Daly, Mr. Peter Denis male 51.0 0 0 113055 26.5500 E17 S 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 0 17466 25.9292 D17 S 868 0 1 Roebling, Mr. Washington Augustus II male 31.0 0 0 PC 17590 50.4958 A24 S 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 1 11751 52.5542 D35 S 873 0 1 Carlsson, Mr. Frans Olof male 33.0 0 0 695 5.0000 B51 B53 B55 S 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 1 11767 83.1583 C50 C 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C 183 rows × 11 columns

1.3 保存數(shù)據(jù)
1.3.1 任務(wù)一：將你加載并做出改變的數(shù)據(jù)，在工作目錄下保存為一個新文件train_chinese.csv

df.to_csv("train_chinese.csv" "encoding=‘utf-8")

注意：不同的操作系統(tǒng)保存下來可能會有亂碼。大家可以加入"encoding=“GBK” 或者 "encoding = “uft-8"”

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