pandas快速手册

标签：pyplot   方法   to_date   nan   obj   尺寸   taf   outer   数字   

import xgboost as xgb
from sklearn.metrics import accuracy_score
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

pima = pd.read_csv("D:\\xgbtest\\pima-indians-diabetes.csv") 

#取某列
pima['Outcome']

0      1
1      0
2      1
3      0
4      1
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 768, dtype: int64

#取某行
pima.iloc[0]

Pregnancies                   6.000
Glucose                     148.000
BloodPressure                72.000
SkinThickness                35.000
Insulin                       0.000
BMI                          33.600
DiabetesPedigreeFunction      0.627
Age                          50.000
Outcome                       1.000
Name: 0, dtype: float64

#x选出多个列
pima.loc[:,['Pregnancies','Glucose','BMI']]

#某个位置的值，纯整数索引
pima.iloc[[0],[0]]

fig=plt.figure()
ax=fig.add_subplot(111)
x=pima['BloodPressure']
y=pima['Outcome']
ax.plot(x,y,color='lightblue',linewidth=1)
plt.show()

#查看部分data
pima.head() 

# 对于非数字类型的数据（字符型 数据），可以使用pima.['这里填带统计的标签'].value_counts()统计分类数目

# 可以得到统计数目，得到平均数、方差等特征（当然是针对数字类型的数据）
pima.describe()

# 可以显示该标签下的数据分布，50表示y轴的间隔，以直方图显示，横轴表示数值范围，y轴表示数量
# bins指bin(箱子)的个数，即每张图柱子的个数
# figsize指每张图的尺寸大小

pima.hist(bins=50,figsize=(20,15))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F597CC988>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F59690348>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5987B7C8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F59053208>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F59080BC8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F590BA5C8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F590E8E48>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F59113F88>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5911EB88>]],
      dtype=object)

# 可以显示该标签下的数值分布，观察分布是否均衡 箱形图或盒图
pima.boxplot(column='Pregnancies')

<matplotlib.axes._subplots.AxesSubplot at 0x10f5a32b888>

# 可以将标签1下的数据再按照标签2进行数值分布绘制
pima.boxplot(column='BloodPressure', by = 'Outcome')

<matplotlib.axes._subplots.AxesSubplot at 0x10f5a9660c8>

#查看两列相关性
pima['BloodPressure'].corr(pima['Age'])

0.23952794642136363

#查看两列相关性
pima.corr()

#生成排名
pima['Pregnancies'].rank()

0      574.5
1      179.0
2      663.5
3      179.0
4       56.0
       ...  
763    722.5
764    298.0
765    521.0
766    179.0
767    179.0
Name: Pregnancies, Length: 768, dtype: float64

# rolling()遍历的是移动窗口的数据 由于窗口大小为3(window)，前两个元素有空值，第三个元素的值将是n，n-1和n-2元素的平均值。  下例中 取得均值
pima['Pregnancies'].rolling(window=3).mean()

0           NaN
1           NaN
2      5.000000
3      3.333333
4      3.000000
         ...   
763    9.333333
764    7.000000
765    5.666667
766    2.666667
767    2.333333
Name: Pregnancies, Length: 768, dtype: float64

# 数据补全
df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f','h'],columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print (df)
print (df['one'].fillna(df['one'].median()))  #用中位数填充缺失值

        one       two     three
a  0.018628 -0.684334  1.280671
b       NaN       NaN       NaN
c  1.043673 -1.065631 -0.107962
d       NaN       NaN       NaN
e  0.321906  1.229281 -0.599733
f  0.094914 -2.588472 -0.032368
g       NaN       NaN       NaN
h  1.349480 -0.996243  1.670260
a    0.018628
b    0.321906
c    1.043673
d    0.321906
e    0.321906
f    0.094914
g    0.321906
h    1.349480
Name: one, dtype: float64

df = pd.DataFrame({'one':[10,20,30,40,50,2000],'two':[1000,0,30,40,50,60]})
#替换 用10 换1000；60换2000.可以同时替换多个值
print (df.replace({1000:10,2000:60}))     

   one  two
0   10   10
1   20    0
2   30   30
3   40   40
4   50   50
5   60   60

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings','kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

#按'Team'分组，并打印结果
print (df.groupby('Team').groups)

{'Devils': Int64Index([2, 3], dtype='int64'), 'Kings': Int64Index([4, 6, 7], dtype='int64'), 'Riders': Int64Index([0, 1, 8, 11], dtype='int64'), 'Royals': Int64Index([9, 10], dtype='int64'), 'kings': Int64Index([5], dtype='int64')}

#按'Year'分组，并迭代输出
grouped = df.groupby('Year')
for name,group in grouped:
    print (name)
    print (group)

2014
     Team  Rank  Year  Points
0  Riders     1  2014     876
2  Devils     2  2014     863
4   Kings     3  2014     741
9  Royals     4  2014     701
2015
      Team  Rank  Year  Points
1   Riders     2  2015     789
3   Devils     3  2015     673
5    kings     4  2015     812
10  Royals     1  2015     804
2016
     Team  Rank  Year  Points
6   Kings     1  2016     756
8  Riders     2  2016     694
2017
      Team  Rank  Year  Points
7    Kings     1  2017     788
11  Riders     2  2017     690

# 使用get_group()方法，可以选择一个组。   
print (grouped.get_group(2014))

     Team  Rank  Year  Points
0  Riders     1  2014     876
2  Devils     2  2014     863
4   Kings     3  2014     741
9  Royals     4  2014     701

# 聚合函数为每个组返回单个聚合值
grouped = df.groupby('Year')
print (grouped['Points'].agg(np.mean))

Year
2014    795.25
2015    769.50
2016    725.00
2017    739.00
Name: Points, dtype: float64

# 查看每个分组的大小的方法
grouped = df.groupby('Team')
print (grouped.agg(np.size))

        Rank  Year  Points
Team                      
Devils     2     2       2
Kings      3     3       3
Riders     4     4       4
Royals     2     2       2
kings      1     1       1

# 通过分组系列，还可以传递函数的列表或字典来进行聚合
grouped = df.groupby('Team')
agg = grouped['Points'].agg([np.sum, np.mean, np.std])
print (agg)

         sum        mean         std
Team                                
Devils  1536  768.000000  134.350288
Kings   2285  761.666667   24.006943
Riders  3049  762.250000   88.567771
Royals  1505  752.500000   72.831998
kings    812  812.000000         NaN

# 过滤根据定义的标准过滤数据并返回数据的子集
filter = df.groupby('Team').filter(lambda x: len(x) >= 3)
print (filter)

      Team  Rank  Year  Points
0   Riders     1  2014     876
1   Riders     2  2015     789
4    Kings     3  2014     741
6    Kings     1  2016     756
7    Kings     1  2017     788
8   Riders     2  2016     694
11  Riders     2  2017     690

# 类似SQL的两个表连接
left = pd.DataFrame({
         'id':[1,2,3,4,5],
         'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
         'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame(
         {'id':[1,2,3,4,5],
         'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
         'subject_id':['sub2','sub4','sub3','sub6','sub5']})
rs = pd.merge(left,right,on='id')  #等值连接
print(rs)

   id  Name_x subject_id_x Name_y subject_id_y
0   1    Alex         sub1  Billy         sub2
1   2     Amy         sub2  Brian         sub4
2   3   Allen         sub4   Bran         sub3
3   4   Alice         sub6  Bryce         sub6
4   5  Ayoung         sub5  Betty         sub5

# 合并多个键上的两个数据框
rs = pd.merge(left,right,on=['id','subject_id'])
print(rs)

   id  Name_x subject_id Name_y
0   4   Alice       sub6  Bryce
1   5  Ayoung       sub5  Betty

#left 相当于 LEFT OUTER JOIN
# right 相当于 RIGHT OUTER JOIN
# outer 相当于 FULL OUTER JOIN
# inner 相当于INNER JOIN
rs = pd.merge(left, right, on='subject_id', how='left')
print (rs)

   id_x  Name_x subject_id  id_y Name_y
0     1    Alex       sub1   NaN    NaN
1     2     Amy       sub2   1.0  Billy
2     3   Allen       sub4   2.0  Brian
3     4   Alice       sub6   4.0  Bryce
4     5  Ayoung       sub5   5.0  Betty

#生成时间
#取当前时间
print(pd.datetime.now())

2020-02-29 11:15:30.372039

# 创建一个时间戳
time = pd.Timestamp('2018-11-01')
print(time)

2018-11-01 00:00:00

# 转换成整数或浮动时期。这些的默认单位是纳秒(因为这些是如何存储时间戳的)。 
time = pd.Timestamp(1588686880,unit='s')
print(time)

2020-05-05 13:54:40

# 创建一个时间范围
time = pd.date_range("12:00", "23:59", freq="30min").time
print(time)

[datetime.time(12, 0) datetime.time(12, 30) datetime.time(13, 0)
 datetime.time(13, 30) datetime.time(14, 0) datetime.time(14, 30)
 datetime.time(15, 0) datetime.time(15, 30) datetime.time(16, 0)
 datetime.time(16, 30) datetime.time(17, 0) datetime.time(17, 30)
 datetime.time(18, 0) datetime.time(18, 30) datetime.time(19, 0)
 datetime.time(19, 30) datetime.time(20, 0) datetime.time(20, 30)
 datetime.time(21, 0) datetime.time(21, 30) datetime.time(22, 0)
 datetime.time(22, 30) datetime.time(23, 0) datetime.time(23, 30)]

# 改变时间的频率
time = pd.date_range("12:00", "23:59", freq="H").time
print(time)

[datetime.time(12, 0) datetime.time(13, 0) datetime.time(14, 0)
 datetime.time(15, 0) datetime.time(16, 0) datetime.time(17, 0)
 datetime.time(18, 0) datetime.time(19, 0) datetime.time(20, 0)
 datetime.time(21, 0) datetime.time(22, 0) datetime.time(23, 0)]

# 要转换类似日期的对象
time = pd.to_datetime(pd.Series(['Jul 31, 2009','2019-10-10','2019/1/2','20190101', None]))
print(time)

0   2009-07-31
1   2019-10-10
2   2019-01-02
3   2019-01-01
4          NaT
dtype: datetime64[ns]

# 创建一个日期范围通过指定周期和频率，使用date.range()函数就可以创建日期序列。 默认情况下，范围的频率是天。
datelist = pd.date_range('2020/11/21', periods=5)
print(datelist)

DatetimeIndex(['2020-11-21', '2020-11-22', '2020-11-23', '2020-11-24',
               '2020-11-25'],
              dtype='datetime64[ns]', freq='D')

# 更改日期频率
datelist = pd.date_range('2020/11/21', periods=5,freq='M')
print(datelist)

DatetimeIndex(['2020-11-30', '2020-12-31', '2021-01-31', '2021-02-28',
               '2021-03-31'],
              dtype='datetime64[ns]', freq='M')

# bdate_range()用来表示商业日期范围，不同于date_range()，它不包括星期六和星期天。观察到11月3日以后，日期跳至11月6日，不包括4日和5日(因为它们是周六和周日)。
datelist = pd.date_range('2011/11/03', periods=5)
print(datelist)

DatetimeIndex(['2011-11-03', '2011-11-04', '2011-11-05', '2011-11-06',
               '2011-11-07'],
              dtype='datetime64[ns]', freq='D')

#时间范围
start = pd.datetime(2017, 11, 1)
end = pd.datetime(2017, 11, 5)
dates = pd.date_range(start, end)
print(dates)

DatetimeIndex(['2017-11-01', '2017-11-02', '2017-11-03', '2017-11-04',
               '2017-11-05'],
              dtype='datetime64[ns]', freq='D')

# 时间差（Timedelta）
timediff = pd.Timedelta('2 days 2 hours 15 minutes 30 seconds')
print(timediff)

2 days 02:15:30

timediff = pd.Timedelta(6,unit='h')
print(timediff)

0 days 06:00:00

timediff = pd.Timedelta(days=2)
print(timediff)

2 days 00:00:00

#构建两组数据
s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ])
df = pd.DataFrame(dict(A = s, B = td))
print(df)

           A      B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days

# 相加操作
df['C']=df['A']+df['B']
print(df)

           A      B          C
0 2012-01-01 0 days 2012-01-01
1 2012-01-02 1 days 2012-01-03
2 2012-01-03 2 days 2012-01-05

# 相减操作
df['D']=df['C']-df['B']
print(df)

           A      B          C          D
0 2012-01-01 0 days 2012-01-01 2012-01-01
1 2012-01-02 1 days 2012-01-03 2012-01-02
2 2012-01-03 2 days 2012-01-05 2012-01-03

#基本绘图
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x10f5dbe95c8>

# 条形图
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
df.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x10f5dc624c8>

# 堆积条形图
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
df.plot.bar(stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x10f5de41e48>

# 水平条形图
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
df.plot.barh(stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x10f5df29d88>

# 直方图
df = pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
df.plot.hist(bins=50, figsize=(5,5))
# bins指bin(箱子)的个数，即每张图柱子的个数
# figsize指每张图的尺寸大小

<matplotlib.axes._subplots.AxesSubplot at 0x10f5f644248>

# 要为每列绘制不同的直方图
# df=pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
df.hist(bins=20)

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5FACE7C8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5FACE5C8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5FB3C108>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000010F5FB72AC8>]],
      dtype=object)

# 箱形图
# Boxplot可以绘制调用Series.box.plot()和DataFrame.box.plot()或DataFrame.boxplot()来可视化每列中值的分布。
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot.box()

<matplotlib.axes._subplots.AxesSubplot at 0x10f5fbd2788>

# 区域块图形 可以使用Series.plot.area()或DataFrame.plot.area()方法创建区域图形
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area()

<matplotlib.axes._subplots.AxesSubplot at 0x10f5fd7f708>

# 散点图形
df = pd.DataFrame(np.random.rand(20, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')

<matplotlib.axes._subplots.AxesSubplot at 0x10f611e0e08>

#折线图
df = pd.DataFrame({'a':np.random.rand(10)})
df.plot(kind='line')

<matplotlib.axes._subplots.AxesSubplot at 0x10f6145f588>

# 饼状图
df = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])
df.plot.pie(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000010F6157B588>],
      dtype=object)

pandas快速手册

标签：pyplot   方法   to_date   nan   obj   尺寸   taf   outer   数字   

原文地址：https://www.cnblogs.com/duoba/p/12382788.html