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Python数据分析招式:pandas库提取清洗排序-1

发布时间:2021-11-23 点击数:170

要点:


数据的基本处理

数据的提取

数据的初步清洗

数据的排序

泰坦尼克数据集下载地址:

地址1(需要注册): https://www.kaggle.com/c/titanic/data

地址2(百度网盘): https://pan.baidu.com/s/1Vp0QmVLu43_Hb9jHR2FKXg

密码: rdfr


导入数据

# -*- coding: utf-8 -*-  # @File    : 泰坦尼克数据分析.py # @Date    : 2018-06-03  import numpy as np import pandas as pd  file = "data/train.csv" df = pd.DataFrame(pd.read_csv(file))

1、数据的基本处理

#  形状 print(df.shape) # (891, 12)  # 查看前3行 print(df.head(3)) """  PassengerId  Survived  Pclass    ...        Fare Cabin  Embarked 0            1         0       3    ...      7.2500   NaN         S 1            2         1       1    ...     71.2833   C85         C 2            3         1       3    ...      7.9250   NaN         S  [3 rows x 12 columns] """  # 查看后3行 print(df.tail(3)) """  PassengerId  Survived  Pclass    ...      Fare Cabin  Embarked 888          889         0       3    ...     23.45   NaN         S 889          890         1       1    ...     30.00  C148         C 890          891         0       3    ...      7.75   NaN         Q  [3 rows x 12 columns] """  # 信息 print(df.info()) """ <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 """  # 整体描述 print(df.describe()) """  PassengerId    Survived     ...           Parch        Fare count   891.000000  891.000000     ...      891.000000  891.000000 mean    446.000000    0.383838     ...        0.381594   32.204208 std     257.353842    0.486592     ...        0.806057   49.693429 min       1.000000    0.000000     ...        0.000000    0.000000 25%     223.500000    0.000000     ...        0.000000    7.910400 50%     446.000000    0.000000     ...        0.000000   14.454200 75%     668.500000    1.000000     ...        0.000000   31.000000 max     891.000000    1.000000     ...        6.000000  512.329200  [8 rows x 7 columns] """  # 查看数据集的空值 print(df.isnull().sum()) """ PassengerId      0 Survived         0 Pclass           0 Name             0 Sex              0 Age            177 SibSp            0 Parch            0 Ticket           0 Fare             0 Cabin          687 Embarked         2 dtype: int64 """  # 唯一值 print(df["Pclass"].unique()) # [3 1 2]

2、数据的提取

# 按照索引的值提取数据 print(df.loc[630]) """ PassengerId                                     631 Survived                                          1 Pclass                                            1 Name           Barkworth, Mr. Algernon Henry Wilson Sex                                            male Age                                              80 SibSp                                             0 Parch                                             0 Ticket                                        27042 Fare                                             30 Cabin                                           A23 Embarked                                          S Name: 630, dtype: object """  # 取部分行和列 第二三四行和前5列 print(df.iloc[2:5, :5]) """  PassengerId   ...       Sex 2            3   ...    female 3            4   ...    female 4            5   ...      male  [3 rows x 5 columns] """  # 照条件提取  仓位为小于2的,并且性别为女性的数据 print(df[(df["Pclass"]<=2)&(df["Sex"]=="female")].head(3)) """  PassengerId  Survived  Pclass    ...        Fare Cabin  Embarked 1            2         1       1    ...     71.2833   C85         C 3            4         1       1    ...     53.1000  C123         S 9           10         1       2    ...     30.0708   NaN         C  [3 rows x 12 columns] """

3、数据的清洗

# 删除空值 print(df.shape)  # (891, 12) ret = df.dropna(how="any") print(ret.shape)  # (183, 12) print(df.shape)  # (891, 12)  # 填充空值 ret = df.fillna(value=0) print(df.loc[633]) print(ret.loc[633]) """ PassengerId                              634 Survived                                   0 Pclass                                     1 Name           Parr, Mr. William Henry Marsh Sex                                     male Age                                      NaN SibSp                                      0 Parch                                      0 Ticket                                112052 Fare                                       0 Cabin                                    NaN Embarked                                   S Name: 633, dtype: object PassengerId                              634 Survived                                   0 Pclass                                     1 Name           Parr, Mr. William Henry Marsh Sex                                     male Age                                        0 SibSp                                      0 Parch                                      0 Ticket                                112052 Fare                                       0 Cabin                                      0 Embarked                                   S Name: 633, dtype: object """  #用数据集里面的年龄均值来填充空值 ret = df['Age'].fillna(df['Age'].mean()) print(ret.shape)  # (891,)  # 对字符的处理,比如大小写的转换 print(df["Name"].map(str.upper).head(3)) """ 0                              BRAUND, MR. OWEN HARRIS 1    CUMINGS, MRS. JOHN BRADLEY (FLORENCE BRIGGS TH... 2                               HEIKKINEN, MISS. LAINA Name: Name, dtype: object """  # 对字符串的快速映射转换 df['Pclass']=df['Pclass'].map({1:'一等舱',2:'二等舱',3:'三等舱'}) print(df.head(3)) """  PassengerId  Survived Pclass    ...        Fare Cabin  Embarked 0            1         0    三等舱    ...      7.2500   NaN         S 1            2         1    一等舱    ...     71.2833   C85         C 2            3         1    三等舱    ...      7.9250   NaN         S  [3 rows x 12 columns] """  # 对数据集中的数据格式的改变 print(df.dtypes) """ PassengerId      int64 Survived         int64 Pclass          object Name            object Sex             object Age            float64 SibSp            int64 Parch            int64 Ticket          object Fare           float64 Cabin           object Embarked        object dtype: object """  ret = df['Fare'].astype('int') #把原来的float64->int print(ret.dtypes) # int32  # 更改列的名字 ret = df.rename(columns={'Survived':'是否获救'}) print(ret.head(3)) """  PassengerId  是否获救 Pclass    ...        Fare Cabin  Embarked 0            1     0    三等舱    ...      7.2500   NaN         S 1            2     1    一等舱    ...     71.2833   C85         C 2            3     1    三等舱    ...      7.9250   NaN         S  [3 rows x 12 columns] """  # 去掉重复值 # #比如我们想知道登船的类别,去掉所有重复的数据 ret = df['Embarked'].drop_duplicates() print(ret) """ 0       S 1       C 5       Q 61    NaN Name: Embarked, dtype: object """  # 数据的代替,替换 df['Sex']=df['Sex'].replace('male','男') print(df["Sex"].head(3)) """ 0         男 1    female 2    female Name: Sex, dtype: object """

4、数据的排序

# 按照年龄进行降序排列 print(df.sort_values(by=['Age'],ascending=False)["Age"].head(3)) """ 630    80.0 851    74.0 493    71.0 Name: Age, dtype: float64 """ # 按照index来排序 print(df.sort_index(ascending=False).head(3)) """  PassengerId  Survived Pclass    ...      Fare Cabin  Embarked 890          891         0    三等舱    ...      7.75   NaN         Q 889          890         1    一等舱    ...     30.00  C148         C 888          889         0    三等舱    ...     23.45   NaN         S """