Pandas之:Pandas高级教程以铁达尼号真实数据为例
目录
简介
今天我们会讲解一下Pandas的高级教程，包括读写文件、选取子集和图形表示等。
读写文件
数据处理的一个关键步骤就是读取文件进行分析，然后将分析处理结果再次写入文件。
Pandas支持多种文件格式的读取和写入：
In [108]: pd.read_ read_clipboard() read_excel() read_fwf() read_hdf() read_json read_parquet read_sas read_sql_query read_stata read_csv read_feather() read_gbq() read_html read_msgpack read_pickle read_sql read_sql_table read_table
接下来我们会以Pandas官网提供的Titanic.csv为例来讲解Pandas的使用。
Titanic.csv提供了800多个泰坦利特号上乘客的信息，是一个891 rows x 12 columns的矩阵。
我们使用Pandas来读取这个csv：
In [5]: titanic=pd.read_csv("titanic.csv")
read_csv方法会将csv文件转换成为pandas 的DataFrame。
默认情况下我们直接使用DF变量，会默认展示前5行和后5行数据：
In [3]: titanic Out[3]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S .. ... ... ... ... ... ... ... ... ... ... ... 886 887 0 2 Montvila, Rev. Juozas male ... 0 211536 13.0000 NaN S 887 888 1 1 Graham, Miss. Margaret Edith female ... 0 112053 30.0000 B42 S 888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female ... 2 W./C. 6607 23.4500 NaN S 889 890 1 1 Behr, Mr. Karl Howell male ... 0 111369 30.0000 C148 C 890 891 0 3 Dooley, Mr. Patrick male ... 0 370376 7.7500 NaN Q [891 rows x 12 columns]
可以使用head(n)和tail(n)来指定特定的行数：
In [4]: titanic.head(8) Out[4]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male ... 0 330877 8.4583 NaN Q 6 7 0 1 McCarthy, Mr. Timothy J male ... 0 17463 51.8625 E46 S 7 8 0 3 Palsson, Master. Gosta Leonard male ... 1 349909 21.0750 NaN S [8 rows x 12 columns]
使用dtypes可以查看每一列的数据类型：
In [5]: titanic.dtypes Out[5]: PassengerId int64 Survived int64 Pclass int64 Name object Sex object Age float64 SibSp int64 Parch int64 Ticket object Fare float64 Cabin object Embarked object dtype: object
使用to_excel可以将DF转换为excel文件，使用read_excel可以再次读取excel文件：
In [11]: titanic.to_excel('titanic.xlsx', sheet_name='passengers', index=False) In [12]: titanic = pd.read_excel('titanic.xlsx', sheet_name='passengers')
使用info()可以来对DF进行一个初步的统计：
In [14]: titanic.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
DF的选择
选择列数据
DF的head或者tail方法只能显示所有的列数据，下面的方法可以选择特定的列数据。
In [15]: ages = titanic["Age"] In [16]: ages.head() Out[16]: 0 22.0 1 38.0 2 26.0 3 35.0 4 35.0 Name: Age, dtype: float64
每一列都是一个Series：
In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series In [7]: titanic["Age"].shape Out[7]: (891,)
还可以多选：
In [8]: age_sex = titanic[["Age", "Sex"]] In [9]: age_sex.head() Out[9]: Age Sex 0 22.0 male 1 38.0 female 2 26.0 female 3 35.0 female 4 35.0 male
如果选择多列的话，返回的结果就是一个DF类型：
In [10]: type(titanic[["Age", "Sex"]]) Out[10]: pandas.core.frame.DataFrame In [11]: titanic[["Age", "Sex"]].shape Out[11]: (891, 2)
选择行数据
上面我们讲到了怎么选择列数据，下面我们来看看怎么选择行数据：
选择客户年龄大于35岁的：
In [12]: above_35 = titanic[titanic["Age"] > 35] In [13]: above_35.head() Out[13]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 6 7 0 1 McCarthy, Mr. Timothy J male ... 0 17463 51.8625 E46 S 11 12 1 1 Bonnell, Miss. Elizabeth female ... 0 113783 26.5500 C103 S 13 14 0 3 Andersson, Mr. Anders Johan male ... 5 347082 31.2750 NaN S 15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female ... 0 248706 16.0000 NaN S [5 rows x 12 columns]
使用isin选择Pclass在2和3的所有客户：
In [16]: class_23 = titanic[titanic["Pclass"].isin([2, 3])] In [17]: class_23.head() Out[17]: PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
上面的isin等于：
In [18]: class_23 = titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)]
筛选Age不是空的：
In [20]: age_no_na = titanic[titanic["Age"].notna()] In [21]: age_no_na.head() Out[21]: PassengerId Survived Pclass Name Sex ... Parch Ticket Fare Cabin Embarked 0 1 0 3 Braund, Mr. Owen Harris male ... 0 A/5 21171 7.2500 NaN S 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female ... 0 PC 17599 71.2833 C85 C 2 3 1 3 Heikkinen, Miss. Laina female ... 0 STON/O2. 3101282 7.9250 NaN S 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female ... 0 113803 53.1000 C123 S 4 5 0 3 Allen, Mr. William Henry male ... 0 373450 8.0500 NaN S [5 rows x 12 columns]
同时选择行和列
我们可以同时选择行和列。
使用loc和iloc可以进行行和列的选择，他们两者的区别是loc是使用名字进行选择，iloc是使用数字进行选择。
选择age>35的乘客名：
In [23]: adult_names = titanic.loc[titanic["Age"] > 35, "Name"] In [24]: adult_names.head() Out[24]: 1 Cumings, Mrs. John Bradley (Florence Briggs Th... 6 McCarthy, Mr. Timothy J 11 Bonnell, Miss. Elizabeth 13 Andersson, Mr. Anders Johan 15 Hewlett, Mrs. (Mary D Kingcome) Name: Name, dtype: object
loc中第一个值表示行选择，第二个值表示列选择。
使用iloc进行选择：
In [25]: titanic.iloc[9:25, 2:5] Out[25]: Pclass Name Sex 9 2 Nasser, Mrs. Nicholas (Adele Achem) female 10 3 Sandstrom, Miss. Marguerite Rut female 11 1 Bonnell, Miss. Elizabeth female 12 3 Saundercock, Mr. William Henry male 13 3 Andersson, Mr. Anders Johan male .. ... ... ... 20 2 Fynney, Mr. Joseph J male 21 2 Beesley, Mr. Lawrence male 22 3 McGowan, Miss. Anna "Annie" female 23 1 Sloper, Mr. William Thompson male 24 3 Palsson, Miss. Torborg Danira female [16 rows x 3 columns]
使用plots作图
怎么将DF转换成为多样化的图形展示呢？
要想在命令行中使用matplotlib作图，那么需要启动ipython的QT环境：
ipython qtconsole --pylab=inline
直接使用plot来展示一下上面我们读取的乘客信息：
import matplotlib.pyplot as plt import pandas as pd titanic = pd.read_excel('titanic.xlsx', sheet_name='passengers') titanic.plot()
横坐标就是DF中的index，列坐标是各个列的名字。注意上面的列只展示的是数值类型的。
我们只展示age信息：
titanic['Age'].plot()
默认的是柱状图，我们可以转换图形的形式，比如点图：
titanic.plot.scatter(x="PassengerId",y="Age", alpha=0.5)
选择数据中的PassengerId作为x轴，age作为y轴：
除了散点图，还支持很多其他的图像：
[method_name for method_name in dir(titanic.plot) if not method_name.startswith("_")] Out[11]: ['area', 'bar', 'barh', 'box', 'density', 'hexbin', 'hist', 'kde', 'line', 'pie', 'scatter']
再看一个box图：
titanic['Age'].plot.box()
可以看到，乘客的年龄大多集中在20-40岁之间。
还可以将选择的多列分别作图展示：
titanic.plot.area(figsize=(12, 4), subplots=True)
指定特定的列：
titanic[['Age','Pclass']].plot.area(figsize=(12, 4), subplots=True)
还可以先画图，然后填充：
fig, axs = plt.subplots(figsize=(12, 4));
先画一个空的图，然后对其进行填充：
titanic['Age'].plot.area(ax=axs); axs.set_ylabel("Age"); fig
使用现有的列创建新的列
有时候，我们需要对现有的列进行变换，以得到新的列，比如我们想添加一个Age2列，它的值是Age列+10，则可以这样：
titanic["Age2"]=titanic["Age"]+10; titanic[["Age","Age2"]].head() Out[34]: Age Age2 0 22.0 32.0 1 38.0 48.0 2 26.0 36.0 3 35.0 45.0 4 35.0 45.0
还可以对列进行重命名：
titanic_renamed = titanic.rename( ...: columns={"Age": "Age2", ...: "Pclass": "Pclas2"})
列名转换为小写：
titanic_renamed = titanic_renamed.rename(columns=str.lower)
进行统计
我们来统计下乘客的平均年龄：
titanic["Age"].mean() Out[35]: 29.69911764705882
选择中位数：
titanic[["Age", "Fare"]].median() Out[36]: Age 28.0000 Fare 14.4542 dtype: float64
更多信息：
titanic[["Age", "Fare"]].describe() Out[37]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38.000000 31.000000 max 80.000000 512.329200
使用agg指定特定的聚合方法：
titanic.agg({'Age': ['min', 'max', 'median', 'skew'],'Fare': ['min', 'max', 'median', 'mean']}) Out[38]: Age Fare max 80.000000 512.329200 mean NaN 32.204208 median 28.000000 14.454200 min 0.420000 0.000000 skew 0.389108 NaN
可以使用groupby：
titanic[["Sex", "Age"]].groupby("Sex").mean() Out[39]: Age Sex female 27.915709 male 30.726645
groupby所有的列：
titanic.groupby("Sex").mean() Out[40]: PassengerId Survived Pclass Age SibSp Parch Sex female 431.028662 0.742038 2.159236 27.915709 0.694268 0.649682 male 454.147314 0.188908 2.389948 30.726645 0.429809 0.235702
groupby之后还可以选择特定的列：
titanic.groupby("Sex")["Age"].mean() Out[41]: Sex female 27.915709 male 30.726645 Name: Age, dtype: float64
可以分类进行count：
titanic["Pclass"].value_counts() Out[42]: 3 491 1 216 2 184 Name: Pclass, dtype: int64
上面等同于：
titanic.groupby("Pclass")["Pclass"].count()
DF重组
可以根据某列进行排序：
titanic.sort_values(by="Age").head() Out[43]: PassengerId Survived Pclass Name Sex \ 803 804 1 3 Thomas, Master. Assad Alexander male 755 756 1 2 Hamalainen, Master. Viljo male 644 645 1 3 Baclini, Miss. Eugenie female 469 470 1 3 Baclini, Miss. Helene Barbara female 78 79 1 2 Caldwell, Master. Alden Gates male
根据多列排序：
titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head() Out[44]: PassengerId Survived Pclass Name Sex Age \ 851 852 0 3 Svensson, Mr. Johan male 74.0 116 117 0 3 Connors, Mr. Patrick male 70.5 280 281 0 3 Duane, Mr. Frank male 65.0 483 484 1 3 Turkula, Mrs. (Hedwig) female 63.0 326 327 0 3 Nysveen, Mr. Johan Hansen male 61.0
选择特定的行和列数据，下面的例子我们将会选择性别为女性的部分数据：
female=titanic[titanic['Sex']=='female'] female_subset=female[["Age","Pclass","PassengerId","Survived"]].sort_values(["Pclass"]).groupby(["Pclass"]).head(2) female_subset Out[58]: Age Pclass PassengerId Survived 1 38.0 1 2 1 356 22.0 1 357 1 726 30.0 2 727 1 443 28.0 2 444 1 855 18.0 3 856 1 654 18.0 3 655 0
使用pivot可以进行轴的转换：
female_subset.pivot(columns="Pclass", values="Age") Out[62]: Pclass 1 2 3 1 38.0 NaN NaN 356 22.0 NaN NaN 443 NaN 28.0 NaN 654 NaN NaN 18.0 726 NaN 30.0 NaN 855 NaN NaN 18.0 female_subset.pivot(columns="Pclass", values="Age").plot()
