02_Pandas_数据结构

liu****2026-01-01 16:10

学习目标

  • 掌握Series的常用属性及方法
  • 掌握DataFrame的常用属性及方法
  • 掌握更改Series和DataFrame的方法
  • 掌握如何导入导出数据

1 Series和DataFrame

  • DataFrame和Series是Pandas最基本的两种数据结构
  • DataFrame用来处理结构化数据（SQL数据表，Excel表格）
  • Series用来处理单列数据，也可以把DataFrame看作由Series对象组成的字典或集合

1.1 创建Series

  • 在Pandas中，Series是一维容器，Series表示DataFrame的每一列

  • 可以把DataFrame看作由Series对象组成的字典，其中key是列名，值是Series

  • Series和Python中的列表非常相似，但是它的每个元素的数据类型必须相同

  • 创建 Series 的最简单方法是传入一个Python列表，如果传入的数据类型不统一，最终的dtype通常是object

    python 复制代码 
    import pandas as pd
s = pd.Series(['banana',42])
print(s)

    输出结果

    python 复制代码 
    0    banana
1        42
dtype: object
    • 上面的结果中，左边显示的0,1是Series的索引

  • 创建Series时，可以通过index参数 来指定行索引

    python 复制代码 
    s = pd.Series(['Wes McKinney','Male'],index = ['Name','Gender'])
print(s)

    输出结果

    shell 复制代码 
    Name      Wes McKinney
Gender            Male
dtype: object

1.2 创建 DataFrame

  • 可以使用字典来创建DataFrame

    python 复制代码 
    name_list = pd.DataFrame(
    {'Name':['Tom','Bob'],
     'Occupation':['Teacher','IT Engineer'],
     'age':[28,36]})
print(name_list)

    输出结果

    shell 复制代码 
      Name   Occupation  age
0  Tom      Teacher   28
1   Bob  IT Engineer   36

  • 创建DataFrame的时候可以使用colums参数指定列的顺序，也可以使用index来指定行索引

    python 复制代码 
    name_list = pd.DataFrame(data = {'Occupation':['Teacher','IT Engineer'],'Age':[28,36]},columns=['Age','Occupation'],index=['Tom','Bob'])
print(name_list)

    输出结果

    python 复制代码 
        Age   Occupation
Tom   28      Teacher
Bob    36  IT Engineer

2 Series 常用操作

2.1 Series常用属性

  • 使用 DataFrame的loc 属性获取数据集里的一行，就会得到一个Series对象

    python 复制代码 
    data = pd.read_csv('data/nobel_prizes.csv',index_col='id')
data.head()

    输出结果

    id year category overallMotivation firstname surname motivation share
    941 2017 physics NaN Rainer Weiss "for decisive contributions to the LIGO detect... 2
    942 2017 physics NaN Barry C. Barish "for decisive contributions to the LIGO detect... 4
    943 2017 physics NaN Kip S. Thorne "for decisive contributions to the LIGO detect... 4
    944 2017 chemistry NaN Jacques Dubochet "for developing cryo-electron microscopy for t... 3
    945 2017 chemistry NaN Joachim Frank "for developing cryo-electron microscopy for t... 3

  • 使用行索引标签选择一条记录

    python 复制代码 
    first_row = data.loc[941]
type(first_row)

    输出结果

    shell 复制代码 
    pandas.core.series.Series
    python 复制代码 
    print(first_row)

    输出结果

    shell 复制代码 
    year                                                              2017
category                                                       physics
overallMotivation                                                  NaN
firstname                                                       Rainer
surname                                                          Weiss
motivation           "for decisive contributions to the LIGO detect...
share                                                                2
Name: 941, dtype: object

  • 可以通过 index 和 values属性获取行索引和值

    python 复制代码 
    first_row.index

    输出结果

    shell 复制代码 
    Index(['year', 'category', 'overallMotivation', 'firstname', 'surname',
      'motivation', 'share'],
     dtype='object')
    python 复制代码 
    print(first_row.values)

    输出结果

    shell 复制代码 
    [2017 'physics' nan 'Rainer' 'Weiss'
'"for decisive contributions to the LIGO detector and the observation of gravitational waves"'
2]

  • Series的keys方法，作用个index属性一样

    python 复制代码 
    data.keys()

    输出结果

    shell 复制代码 
    Index(['year', 'category', 'overallMotivation', 'firstname', 'surname',
      'motivation', 'share'],
     dtype='object')

  • Series的一些属性

    |--------------|--------------|
    | 属性 | 说明 |
    | loc | 使用索引值取子集 |
    | iloc | 使用索引位置取子集 |
    | dtype或dtypes | Series内容的类型 |
    | shape | 数据的维数 |
    | size | Series中元素的数量 |
    | values | Series的值 |

2.2 Series常用方法

  • 针对数值型的Series，可以进行常见计算

    python 复制代码 
    share = data.share  # 从DataFrame中 获取Share列（几人获奖）返回Series
share.mean()      #计算几人获奖的平均值

    输出结果

    shell 复制代码 
    1.982665222101842
    python 复制代码 
    share.max() # 计算最大值

    输出结果

    shell 复制代码 
    4
    python 复制代码 
    share.min() # 计算最小值

    输出结果

    shell 复制代码 
    1
    python 复制代码 
    share.std() # 计算标准差

    输出结果

    shell 复制代码 
    0.9324952202244597

  • 通过value_counts()方法，可以返回不同值的条目数量

    python 复制代码 
    movie = pd.read_csv('data/movie.csv')    # 加载电影数据
director = movie['director_name']   # 从电影数据中获取导演名字 返回Series
actor_1_fb_likes = movie['actor_1_facebook_likes'] # 从电影数据中取出主演的facebook点赞数
director.head()  #查看导演Series数据

    输出结果

    shell 复制代码 
    0        James Cameron
1       Gore Verbinski
2           Sam Mendes
3    Christopher Nolan
4          Doug Walker
Name: director_name, dtype: object
    python 复制代码 
    actor_1_fb_likes.head() #查看主演的facebook点赞数

    输出结果

    shell 复制代码 
    0     1000.0
1    40000.0
2    11000.0
3    27000.0
4      131.0
Name: actor_1_facebook_likes, dtype: float64
    python 复制代码 
    pd.set_option('max_rows', 8) # 设置最多显示8行
director.value_counts()      # 统计不同导演指导的电影数量

    输出结果

    shell 复制代码 
    Steven Spielberg    26
Woody Allen         22
Clint Eastwood      20
Martin Scorsese     20
                   ..
Gavin Wiesen         1
Andrew Morahan       1
Luca Guadagnino      1
Richard Montoya      1
Name: director_name, Length: 2397, dtype: int64
    python 复制代码 
    actor_1_fb_likes.value_counts()

    输出结果

    shell 复制代码 
    1000.0     436
11000.0    206
2000.0     189
3000.0     150
         ... 
216.0        1
859.0        1
225.0        1
334.0        1
Name: actor_1_facebook_likes, Length: 877, dtype: int64

  • 通过count()方法可以返回有多少非空值

    python 复制代码 
    director.count()

    输出结果

    shell 复制代码 
    4814
    python 复制代码 
    director.shape

    输出结果

    shell 复制代码 
    (4916,)

  • 通过describe()方法打印描述信息

    python 复制代码 
    actor_1_fb_likes.describe()

    输出结果

    shell 复制代码 
    count      4909.000000
mean       6494.488491
std       15106.986884
min           0.000000
25%         607.000000
50%         982.000000
75%       11000.000000
max      640000.000000
Name: actor_1_facebook_likes, dtype: float64
    python 复制代码 
    director.describe()

    输出结果

    shell 复制代码 
    count                 4814
unique                2397
top       Steven Spielberg
freq                    26
Name: director_name, dtype: object

  • Series的一些方法

    |-----------------|---------------------|
    | 方法 | 说明 |
    | append | 连接两个或多个Series |
    | corr | 计算与另一个Series的相关系数 |
    | cov | 计算与另一个Series的协方差 |
    | describe | 计算常见统计量 |
    | drop_duplicates | 返回去重之后的Series |
    | equals | 判断两个Series是否相同 |
    | hist | 绘制直方图 |
    | isin | Series中是否包含某些值 |
    | min | 返回最小值 |
    | max | 返回最大值 |
    | mean | 返回算术平均值 |
    | median | 返回中位数 |
    | mode | 返回众数 |
    | quantile | 返回指定位置的分位数 |
    | replace | 用指定值代替Series中的值 |
    | sample | 返回Series的随机采样值 |
    | sort_values | 对值进行排序 |
    | to_frame | 把Series转换为DataFrame |
    | unique | 去重返回数组 |

2.3 Series的布尔索引

  • 从Series中获取满足某些条件的数据，可以使用布尔索引

    python 复制代码 
    scientists = pd.read_csv('data/scientists.csv')
print(scientists)

    输出结果

    shell 复制代码 
                      Name        Born        Died  Age          Occupation
0     Rosaline Franklin  1920-07-25  1958-04-16   37             Chemist
1        William Gosset  1876-06-13  1937-10-16   61        Statistician
2  Florence Nightingale  1820-05-12  1910-08-13   90               Nurse
3           Marie Curie  1867-11-07  1934-07-04   66             Chemist
4         Rachel Carson  1907-05-27  1964-04-14   56           Biologist
5             John Snow  1813-03-15  1858-06-16   45           Physician
6           Alan Turing  1912-06-23  1954-06-07   41  Computer Scientist
7          Johann Gauss  1777-04-30  1855-02-23   77       Mathematician
    • 获取大于平均年龄的结果
    python 复制代码 
    ages = scientists['Age']
ages.mean()

    输出结果

    shell 复制代码 
    59.125
    python 复制代码 
    print(ages[ages>ages.mean()])

    输出结果

    shell 复制代码 
    1    61
2    90
3    66
7    77
Name: Age, dtype: int64
    • ages>ages.mean() 分析返回结果
    python 复制代码 
    print(ages>ages.mean())

    输出结果

    shell 复制代码 
    0    False
1     True
2     True
3     True
4    False
5    False
6    False
7     True
Name: Age, dtype: bool
    • 从上面结果可以得知，从Series中获取部分数据，可以通过标签，索引，也可以传入布尔值的列表
    python 复制代码 
    #手动创建布尔值列表
bool_values = [False,True,True,False,False,False,False,False]
ages[bool_values]

    输出结果

    shell 复制代码 
    1    61
2    90
Name: Age, dtype: int64

2.4 Series 的运算

  • Series和数值型变量计算时，变量会与Series中的每个元素逐一进行计算

    python 复制代码 
    ages+100

    输出结果

    shell 复制代码 
    0    137
1    161
2    190
3    166
4    156
5    145
6    141
7    177
Name: Age, dtype: int64
    python 复制代码 
    ages*2

    输出结果

    shell 复制代码 
    0     74
1    122
2    180
3    132
4    112
5     90
6     82
7    154
Name: Age, dtype: int64

  • 两个Series之间进行计算，会根据索引进行。索引不同的元素最终计算的结果会填充成缺失值，用NaN表示

    python 复制代码 
    ages + pd.Series([1,100])

    输出结果

    python 复制代码 
    0     38.0
1    161.0
2      NaN
3      NaN
4      NaN
5      NaN
6      NaN
7      NaN
dtype: float64
    python 复制代码 
    ages * pd.Series([1,100])

    输出结果

    shell 复制代码 
    0      37.0
1    6100.0
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
dtype: float64

3 DataFrame常用操作

3.1 DataFrame的常用属性和方法

  • DataFrame是Pandas中最常见的对象，Series数据结构的许多属性和方法在DataFrame中也一样适用

    python 复制代码 
    movie = pd.read_csv('data/movie.csv')
 # 打印行数和列数
movie.shape

    输出结果

    shell 复制代码 
    (4916, 28)
    python 复制代码 
    # 打印数据的个数
movie.size

    输出结果

    shell 复制代码 
    137648
    python 复制代码 
    # 该数据集的维度
movie.ndim

    输出结果

    shell 复制代码 
    2
    python 复制代码 
    # 该数据集的长度
len(movie)

    输出结果

    shell 复制代码 
    4916
    python 复制代码 
    # 各个列的值的个数
movie.count()

    输出结果

    shell 复制代码 
    color                     4897
director_name             4814
num_critic_for_reviews    4867
duration                  4901
                         ... 
actor_2_facebook_likes    4903
imdb_score                4916
aspect_ratio              4590
movie_facebook_likes      4916
Length: 28, dtype: int64
    python 复制代码 
    # 各列的最小值
movie.min()

    输出结果

    shell 复制代码 
    num_critic_for_reviews     1.00
duration                   7.00
director_facebook_likes    0.00
actor_3_facebook_likes     0.00
                          ... 
actor_2_facebook_likes     0.00
imdb_score                 1.60
aspect_ratio               1.18
movie_facebook_likes       0.00
Length: 16, dtype: float64
    python 复制代码 
    movie.describe()

    输出结果

    num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_1_facebook_likes gross num_voted_users cast_total_facebook_likes facenumber_in_poster num_user_for_reviews budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    count 4867.000000 4901.000000 4814.000000 4893.000000 4909.000000 4.054000e+03 4.916000e+03 4916.000000 4903.000000 4895.000000 4.432000e+03 4810.000000 4903.000000 4916.000000 4590.000000 4916.000000
    mean 137.988905 107.090798 691.014541 631.276313 6494.488491 4.764451e+07 8.264492e+04 9579.815907 1.377320 267.668846 3.654749e+07 2002.447609 1621.923516 6.437429 2.222349 7348.294142
    std 120.239379 25.286015 2832.954125 1625.874802 15106.986884 6.737255e+07 1.383222e+05 18164.316990 2.023826 372.934839 1.002427e+08 12.453977 4011.299523 1.127802 1.402940 19206.016458
    min 1.000000 7.000000 0.000000 0.000000 0.000000 1.620000e+02 5.000000e+00 0.000000 0.000000 1.000000 2.180000e+02 1916.000000 0.000000 1.600000 1.180000 0.000000
    25% 49.000000 93.000000 7.000000 132.000000 607.000000 5.019656e+06 8.361750e+03 1394.750000 0.000000 64.000000 6.000000e+06 1999.000000 277.000000 5.800000 1.850000 0.000000
    50% 108.000000 103.000000 48.000000 366.000000 982.000000 2.504396e+07 3.313250e+04 3049.000000 1.000000 153.000000 1.985000e+07 2005.000000 593.000000 6.600000 2.350000 159.000000
    75% 191.000000 118.000000 189.750000 633.000000 11000.000000 6.110841e+07 9.377275e+04 13616.750000 2.000000 320.500000 4.300000e+07 2011.000000 912.000000 7.200000 2.350000 2000.000000
    max 813.000000 511.000000 23000.000000 23000.000000 640000.000000 7.605058e+08 1.689764e+06 656730.000000 43.000000 5060.000000 4.200000e+09 2016.000000 137000.000000 9.500000 16.000000 349000.000000

3.2 DataFrame的布尔索引

  • 同Series一样，DataFrame也可以使用布尔索引获取数据子集。

    python 复制代码 
    # 使用布尔索引获取部分数据行
movie[movie['duration']>movie['duration'].mean()]

    输出结果

    color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    0 Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    1 Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    2 Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    3 Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    4893 NaN Brandon Landers NaN 143.0 8.0 8.0 Alana Kaniewski 720.0 NaN Drama|Horror|Thriller ... 8.0 English USA NaN 17350.0 2011.0 19.0 3.0 NaN 33
    4898 Color John Waters 73.0 108.0 0.0 105.0 Mink Stole 462.0 180483.0 Comedy|Crime|Horror ... 183.0 English USA NC-17 10000.0 1972.0 143.0 6.1 1.37 0
    4899 Color Olivier Assayas 81.0 110.0 107.0 45.0 Béatrice Dalle 576.0 136007.0 Drama|Music|Romance ... 39.0 French France R 4500.0 2004.0 133.0 6.9 2.35 171
    4902 Color Kiyoshi Kurosawa 78.0 111.0 62.0 6.0 Anna Nakagawa 89.0 94596.0 Crime|Horror|Mystery|Thriller ... 50.0 Japanese Japan NaN 1000000.0 1997.0 13.0 7.4 1.85 817

    2006 rows × 28 columns

  • 可以传入布尔值的列表，来获取部分数据，True所对应的数据会被保留

    python 复制代码 
    movie.head()[[True,True,False,True,False]]

    输出结果

    color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    0 Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    1 Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    3 Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000

    3 rows × 28 columns

3.3 DataFrame的运算

  • 当DataFrame和数值进行运算时，DataFrame中的每一个元素会分别和数值进行运算

    python 复制代码 
    scientists*2

    输出结果

    Name Born Died Age Occupation
    0 Rosaline FranklinRosaline Franklin 1920-07-251920-07-25 1958-04-161958-04-16 74 ChemistChemist
    1 William GossetWilliam Gosset 1876-06-131876-06-13 1937-10-161937-10-16 122 StatisticianStatistician
    2 Florence NightingaleFlorence Nightingale 1820-05-121820-05-12 1910-08-131910-08-13 180 NurseNurse
    3 Marie CurieMarie Curie 1867-11-071867-11-07 1934-07-041934-07-04 132 ChemistChemist
    4 Rachel CarsonRachel Carson 1907-05-271907-05-27 1964-04-141964-04-14 112 BiologistBiologist
    5 John SnowJohn Snow 1813-03-151813-03-15 1858-06-161858-06-16 90 PhysicianPhysician
    6 Alan TuringAlan Turing 1912-06-231912-06-23 1954-06-071954-06-07 82 Computer ScientistComputer Scientist
    7 Johann GaussJohann Gauss 1777-04-301777-04-30 1855-02-231855-02-23 154 MathematicianMathematician
    • 在上面的例子中，数据都是字符串类型的，所有字符串都被复制了一份
    • 上面的例子，如果做其它计算会报错

  • 两个DataFrame之间进行计算，会根据索引进行对应计算

    python 复制代码 
    scientists+scientists

    输出结果

    Name Born Died Age Occupation
    0 Rosaline FranklinRosaline Franklin 1920-07-251920-07-25 1958-04-161958-04-16 74 ChemistChemist
    1 William GossetWilliam Gosset 1876-06-131876-06-13 1937-10-161937-10-16 122 StatisticianStatistician
    2 Florence NightingaleFlorence Nightingale 1820-05-121820-05-12 1910-08-131910-08-13 180 NurseNurse
    3 Marie CurieMarie Curie 1867-11-071867-11-07 1934-07-041934-07-04 132 ChemistChemist
    4 Rachel CarsonRachel Carson 1907-05-271907-05-27 1964-04-141964-04-14 112 BiologistBiologist
    5 John SnowJohn Snow 1813-03-151813-03-15 1858-06-161858-06-16 90 PhysicianPhysician
    6 Alan TuringAlan Turing 1912-06-231912-06-23 1954-06-071954-06-07 82 Computer ScientistComputer Scientist
    7 Johann GaussJohann Gauss 1777-04-301777-04-30 1855-02-231855-02-23 154 MathematicianMathematician
    • 两个DataFrame数据条目数不同时，会根据索引进行计算，索引不匹配的会返回NaN
    python 复制代码 
    first_half = scientists[:4]
scientists+first_half

    输出结果

    Name Born Died Age Occupation
    0 Rosaline FranklinRosaline Franklin 1920-07-251920-07-25 1958-04-161958-04-16 74.0 ChemistChemist
    1 William GossetWilliam Gosset 1876-06-131876-06-13 1937-10-161937-10-16 122.0 StatisticianStatistician
    2 Florence NightingaleFlorence Nightingale 1820-05-121820-05-12 1910-08-131910-08-13 180.0 NurseNurse
    3 Marie CurieMarie Curie 1867-11-071867-11-07 1934-07-041934-07-04 132.0 ChemistChemist
    4 NaN NaN NaN NaN NaN
    5 NaN NaN NaN NaN NaN
    6 NaN NaN NaN NaN NaN
    7 NaN NaN NaN NaN NaN

4 修改Series和DataFrame

4.1 给行索引命名

  • 加载数据文件时，如果不指定行索引，Pandas会自动加上从0开始的索引，可以通过set_index()方法重新设置行索引的名字

    python 复制代码 
    movie = pd.read_csv('data/movie.csv')
movie

    输出结果

    color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    0 Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    1 Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    2 Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    3 Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    4912 Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN Crime|Drama|Mystery|Thriller ... 359.0 English USA TV-14 NaN NaN 593.0 7.5 16.00 32000
    4913 Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN Drama|Horror|Thriller ... 3.0 English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16
    4914 Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 Comedy|Drama|Romance ... 9.0 English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660
    4915 Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 Documentary ... 84.0 English USA PG 1100.0 2004.0 23.0 6.6 1.85 456

    4916 rows × 28 columns

    python 复制代码 
    movie2 = movie.set_index('movie_title')
movie2

    输出结果

    movie_title color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    Avatar Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    Spectre Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    The Following Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN Crime|Drama|Mystery|Thriller ... 359.0 English USA TV-14 NaN NaN 593.0 7.5 16.00 32000
    A Plague So Pleasant Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN Drama|Horror|Thriller ... 3.0 English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16
    Shanghai Calling Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 Comedy|Drama|Romance ... 9.0 English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660
    My Date with Drew Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 Documentary ... 84.0 English USA PG 1100.0 2004.0 23.0 6.6 1.85 456

    4916 rows × 27 columns

  • 加载数据的时候，可以通过通过index_col参数，指定使用某一列数据作为行索引

    python 复制代码 
    pd.read_csv('data/movie.csv', index_col='movie_title')

    输出结果

    movie_title color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    Avatar Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    Spectre Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    The Following Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN Crime|Drama|Mystery|Thriller ... 359.0 English USA TV-14 NaN NaN 593.0 7.5 16.00 32000
    A Plague So Pleasant Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN Drama|Horror|Thriller ... 3.0 English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16
    Shanghai Calling Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 Comedy|Drama|Romance ... 9.0 English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660
    My Date with Drew Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 Documentary ... 84.0 English USA PG 1100.0 2004.0 23.0 6.6 1.85 456

    4916 rows × 27 columns

  • 通过reset_index()方法可以重置索引

    python 复制代码 
    movie2.reset_index()

    输出结果

    movie_title color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    0 Avatar Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    1 Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    2 Spectre Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    3 The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    4912 The Following Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN ... 359.0 English USA TV-14 NaN NaN 593.0 7.5 16.00 32000
    4913 A Plague So Pleasant Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN ... 3.0 English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16
    4914 Shanghai Calling Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 ... 9.0 English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660
    4915 My Date with Drew Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 ... 84.0 English USA PG 1100.0 2004.0 23.0 6.6 1.85 456

    4916 rows × 28 columns

4.2 DataFrame修改行名和列名

  • DataFrame创建之后，可以通过rename()方法对原有的行索引名和列名进行修改

    python 复制代码 
    movie = pd.read_csv('data/movie.csv', index_col='movie_title')
movie.index[:5]

    输出结果

    shell 复制代码 
    Index(['Avatar', 'Pirates of the Caribbean: At World's End', 'Spectre',
      'The Dark Knight Rises', 'Star Wars: Episode VII - The Force Awakens'],
     dtype='object', name='movie_title')
    python 复制代码 
    movie.columns[:5]

    输出结果

    shell 复制代码 
    Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',
      'director_facebook_likes'],
     dtype='object')
    python 复制代码 
    idx_rename = {'Avatar':'Ratava', 'Spectre': 'Ertceps'} 
col_rename = {'director_name':'Director Name', 'num_critic_for_reviews': 'Critical Reviews'} 
movie.rename(index=idx_rename, columns=col_rename).head()

    输出结果

    movie_title color Director Name Critical Reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    Ratava Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    Ertceps Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    Star Wars: Episode VII - The Force Awakens NaN Doug Walker NaN NaN 131.0 NaN Rob Walker 131.0 NaN Documentary ... NaN NaN NaN NaN NaN NaN 12.0 7.1 NaN 0

    5 rows × 27 columns

  • 如果不使用rename()，也可以将index 和 columns属性提取出来，修改之后，再赋值回去

    python 复制代码 
    movie = pd.read_csv('data/movie.csv', index_col='movie_title')
index = movie.index
columns = movie.columns
index_list = index.tolist()
column_list = columns.tolist()

index_list[0] = 'Ratava'
index_list[2] = 'Ertceps'
column_list[1] = 'Director Name'
column_list[2] = 'Critical Reviews'
movie.index = index_list
movie.columns = column_list
movie.head()

    输出结果

    color Director Name Critical Reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres ... num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
    Ratava Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi ... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
    Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy ... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
    Ertceps Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller ... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
    The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller ... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
    Star Wars: Episode VII - The Force Awakens NaN Doug Walker NaN NaN 131.0 NaN Rob Walker 131.0 NaN Documentary ... NaN NaN NaN NaN NaN NaN 12.0 7.1 NaN 0

    5 rows × 27 columns

4.3 添加、删除、插入列

  • 通过dataframe[列名]添加新列

    python 复制代码 
    movie = pd.read_csv('data/movie.csv')
movie['has_seen'] = 0
# 给新列赋值
movie['actor_director_facebook_likes'] = (movie['actor_1_facebook_likes'] + 
                                              movie['actor_2_facebook_likes'] + 
                                              movie['actor_3_facebook_likes'] + 
                                              movie['director_facebook_likes'])

  • 调用drop方法删除列

    python 复制代码 
    movie = movie.drop('actor_director_facebook_likes', axis='columns')

  • 使用insert()方法插入列 loc 新插入的列在所有列中的位置（0,1,2,3...) column=列名 value=值

    python 复制代码 
    movie.insert(loc=0,column='profit',value=movie['gross'] - movie['budget'])
movie

    输出结果

    profit color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross ... language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes has_seen
    0 523505847.0 Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 ... English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000 0
    1 9404152.0 Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 ... English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0 0
    2 -44925825.0 Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 ... English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000 0
    3 198130642.0 Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 ... English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000 0
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    4912 NaN Color NaN 43.0 43.0 NaN 319.0 Valorie Curry 841.0 NaN ... English USA TV-14 NaN NaN 593.0 7.5 16.00 32000 0
    4913 NaN Color Benjamin Roberds 13.0 76.0 0.0 0.0 Maxwell Moody 0.0 NaN ... English USA NaN 1400.0 2013.0 0.0 6.3 NaN 16 0
    4914 NaN Color Daniel Hsia 14.0 100.0 0.0 489.0 Daniel Henney 946.0 10443.0 ... English USA PG-13 NaN 2012.0 719.0 6.3 2.35 660 0
    4915 84122.0 Color Jon Gunn 43.0 90.0 16.0 16.0 Brian Herzlinger 86.0 85222.0 ... English USA PG 1100.0 2004.0 23.0 6.6 1.85 456 0

    4916 rows × 30 columns

5 导出和导入数据

5.1 pickle文件

  • 保存成pickle文件

    • 调用to_pickle方法将以二进制格式保存数据
    • 如要保存的对象是计算的中间结果，或者保存的对象以后会在Python中复用，可把对象保存为.pickle文件
    • 如果保存成pickle文件，只能在python中使用
    • 文件的扩展名可以是.p，.pkl，.pickle
    python 复制代码 
    scientists = pd.read_csv('data/scientists.csv')
scientists.to_pickle('output/scientists_df.pickle')

  • 读取pickle文件

    • 可以使用pd.read_pickle函数读取.pickle文件中的数据
    python 复制代码 
    scientists_name = pd.read_pickle('output/scientists_df.pickle')
print(scientists_name)

5.2 CSV文件

  • 保存成CSV文件

    • CSV(逗号分隔值)是很灵活的一种数据存储格式
    • 在CSV文件中，对于每一行，各列采用逗号分隔
    • 除了逗号，还可以使用其他类型的分隔符，比如TSV文件，使用制表符作为分隔符
    • CSV是数据协作和共享的首选格式
    python 复制代码 
    names.to_csv('output/scientists_name.csv')
#设置分隔符为\t
scientists.to_csv('output/scientists_df.tsv',sep='\t')

  • 不在csv文件中写行名

    python 复制代码 
    scientists.to_csv('output/scientists_df_noindex.csv',index=False)

5.3 Excel文件

  • 保存成Excel文件

    python 复制代码 
    import xlwt
scientists.to_excel('output/scientists_df.xlsx',sheet_name='scientists',index=False)

  • 读取Excel文件

    • 使用pd.read_excel() 读取Excel文件
    python 复制代码 
    pd.read_excel('output/scientists_df.xlsx')

    输出结果

    Name Born Died Age Occupation
    0 Rosaline Franklin 1920-07-25 1958-04-16 37 Chemist
    1 William Gosset 1876-06-13 1937-10-16 61 Statistician
    2 Florence Nightingale 1820-05-12 1910-08-13 90 Nurse
    3 Marie Curie 1867-11-07 1934-07-04 66 Chemist
    4 Rachel Carson 1907-05-27 1964-04-14 56 Biologist
    5 John Snow 1813-03-15 1858-06-16 45 Physician
    6 Alan Turing 1912-06-23 1954-06-07 41 Computer Scientist
    7 Johann Gauss 1777-04-30 1855-02-23 77 Mathematician

  • 注意 pandas读写excel需要额外安装如下三个包

    shell 复制代码 
    pip install -i https://pypi.tuna.tsinghua.edu.cn/simple xlwt 
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple openpyxl
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple xlrd

5.4 其它数据格式

  • feather文件
    • feather是一种文件格式，用于存储二进制对象
    • feather对象也可以加载到R语言中使用
    • feather格式的主要优点是在Python和R语言之间的读写速度要比CSV文件快
    • feather数据格式通常只用中间数据格式，用于Python和R之间传递数据
    • 一般不用做保存最终数据

|--------------|------------------|
| 导出方法 | 说明 |
| to_clipboard | 把数据保存到系统剪贴板，方便粘贴 |
| to_dict | 把数据转换成Python字典 |
| to_hdf | 把数据保存为HDF格式 |
| to_html | 把数据转换成HTML |
| to_json | 把数据转换成JSON字符串 |
| to_sql | 把数据保存到SQL数据库 |

小结

  • 创建Series和DataFrame
    • pd.Series
    • pd.DataFrame
  • Series常用操作
    • 常用属性
      • index
      • values
      • shape,size,dtype
    • 常用方法
      • max(),min(),std()
      • count()
      • describe()
    • 布尔索引
    • 运算
      • 与数值之间进行算数运算会对每一个元素进行计算
      • 两个Series之间进行计算会索引对齐
  • DataFrame常用操作
    • 常用属性
    • 常用方法
    • 布尔索引
    • 运算
  • 更改Series和DataFrame
    • 指定行索引名字
      • dataframe.set_index()
      • dataframe.reset_index()
    • 修改行/列名字
      • dataframe.rename(index=,columns = )
      • 获取行/列索引 转换成list之后，修改list再赋值回去
    • 添加、删除、插入列
      • 添加 dataframe['新列'']
      • 删除 dataframe.drop
      • 插入列 dataframe.insert()
  • 导入导出数据
    • pickle
    • csv
    • Excel
    • feather
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