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简介
明天咱们会解说一下 Pandas 的高级教程，包含读写文件、选取子集和图形示意等。
读写文件
数据处理的一个关键步骤就是读取文件进行剖析，而后将剖析处理结果再次写入文件。
Pandas 反对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
接下来咱们会以 Pandas 官网提供的 Titanic.csv 为例来解说 Pandas 的应用。
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。
咱们应用 Pandas 来读取这个 csv：
In [5]: titanic=pd.read_csv("titanic.csv")read_csv 办法会将 csv 文件转换成为 pandas 的 DataFrame。
默认状况下咱们间接应用 DF 变量，会默认展现前 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]能够应用 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]应用 dtypes 能够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应用 to_excel 能够将 DF 转换为 excel 文件，应用 read_excel 能够再次读取 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')应用 info() 能够来对 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+ KBDF 的抉择
抉择列数据
DF 的 head 或者 tail 办法只能显示所有的列数据，上面的办法能够抉择特定的列数据。
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,)还能够多选：
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如果抉择多列的话，返回的后果就是一个 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)抉择行数据
下面咱们讲到了怎么抉择列数据，上面咱们来看看怎么抉择行数据：
抉择客户年龄大于 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]应用 isin 抉择 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下面的 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]同时抉择行和列
咱们能够同时抉择行和列。
应用 loc 和 iloc 能够进行行和列的抉择，他们两者的区别是 loc 是应用名字进行抉择，iloc 是应用数字进行抉择。
抉择 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: objectloc 中第一个值示意行抉择，第二个值示意列抉择。
应用 iloc 进行抉择：
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]应用 plots 作图
怎么将 DF 转换成为多样化的图形展现呢？
要想在命令行中应用 matplotlib 作图，那么须要启动 ipython 的 QT 环境：
ipython qtconsole --pylab=inline间接应用 plot 来展现一下下面咱们读取的乘客信息：
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。留神下面的列只展现的是数值类型的。
咱们只展现 age 信息：
titanic['Age'].plot()
默认的是柱状图，咱们能够转换图形的模式，比方点图：
titanic.plot.scatter(x="PassengerId",y="Age", alpha=0.5)抉择数据中的 PassengerId 作为 x 轴，age 作为 y 轴：
除了散点图，还反对很多其余的图像：
[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()
能够看到，乘客的年龄大多集中在 20-40 岁之间。
还能够将抉择的多列别离作图展现：
titanic.plot.area(figsize=(12, 4), subplots=True)
指定特定的列：
titanic[['Age','Pclass']].plot.area(figsize=(12, 4), subplots=True)
还能够先画图，而后填充：
fig, axs = plt.subplots(figsize=(12, 4));先画一个空的图，而后对其进行填充：
titanic['Age'].plot.area(ax=axs);
axs.set_ylabel("Age");
fig
应用现有的列创立新的列
有时候，咱们须要对现有的列进行变换，以失去新的列，比方咱们想增加一个 Age2 列，它的值是 Age 列 +10，则能够这样：
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还能够对列进行重命名：
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
咱们来统计下乘客的平均年龄：
titanic["Age"].mean()
Out[35]: 29.69911764705882抉择中位数：
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应用 agg 指定特定的聚合办法：
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能够应用 groupby：
titanic[["Sex", "Age"]].groupby("Sex").mean()
Out[39]:
Age
Sex
female 27.915709
male 30.726645groupby 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 之后还能够抉择特定的列：
titanic.groupby("Sex")["Age"].mean()
Out[41]:
Sex
female 27.915709
male 30.726645
Name: Age, dtype: float64
能够分类进行 count：
titanic["Pclass"].value_counts()
Out[42]:
3 491
1 216
2 184
Name: Pclass, dtype: int64下面等同于：
titanic.groupby("Pclass")["Pclass"].count()DF 重组
能够依据某列进行排序：
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 依据多列排序：
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 抉择特定的行和列数据，上面的例子咱们将会抉择性别为女性的局部数据：
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应用 pivot 能够进行轴的转换：
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()
本文已收录于 http://www.flydean.com/02-python-pandas-advanced/
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