Chp5-1
2019 年 12 月 20 日
In [3]: import pandas as pd my_data = pd.read_csv("C:\Python\Scripts\my_data\Titanic.csv") my_data Out[3]: 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 .. … … … 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 \ 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 .. … … … … 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 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 .. … … … … … 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 [891 rows x 12 columns] In [ ]: import numpy as np from scipy import stats from matplotlib import pyplot as plt In [3]: my_data.head(5) Out[3]: PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 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 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 In [2]: my_dummy=pd.get_dummies((my_data[['Embarked']]),prefix='Embarked') my_dummy.head(5) Out[2]: Embarked_C Embarked_Q Embarked_S 0 0 0 1 1 1 0 0 2 0 0 1 3 0 0 1 4 0 0 1 In [4]: print(my_data.info()) # 这里可以看到， dataframe 的 info 方法能返回对数据的一些总结 <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): PassengerId 891 non-null int64 Survived 891 non-null int64 Pclass 891 non-null int64 Name 891 non-null object Sex 891 non-null object Age 714 non-null float64 SibSp 891 non-null int64 Parch 891 non-null int64 Ticket 891 non-null object Fare 891 non-null float64 Cabin 204 non-null object Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.6+ KB None
