python常用库

pandas

生成数据

（生成的数据data类似于excle 有行标签 index 有列标签 columns ）


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import pandas as pd
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import numpy as np
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s = pd.Series([1,3,6,np.nan,4,1]) #生成一维数组

0 1.0 1 3.0 2 6.0 3 NaN 4 4.0 5 1.0 dtype: float64


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dates = pd.date_range('20160101', periods=6)#index
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df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['A','B','C','D'])# 生成6*4的数据
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print(df)

A B C D 2016-01-01 0.766013 0.709325 -0.177668 1.955910 2016-01-02 -0.458004 -1.522308 0.396516 1.358957 2016-01-03 -1.421787 0.978309 0.677042 1.498006 2016-01-04 0.028752 -1.021694 1.245495 -0.316957 2016-01-05 1.972788 -1.803018 -0.110645 -0.635439 2016-01-06 -0.265201 -0.310657 0.262895 1.532657

选择部分数据输出


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print(df['A'])#    与 df.A 效果相同 按列标签输出

2013-01-01 -0.162028 2013-01-02 -0.247155 2013-01-03 -1.544164 2013-01-04 -1.194200 2013-01-05 2.173732 2013-01-06 -0.501831 Freq: D, Name: A, dtype: float64


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print(df['20130102':'20130104']) # 根据index范围输出 若print(df['A':'C'])则会报错

A B C D 2013-01-02 -0.247155 -1.664754 2.044796 -1.647486 2013-01-03 -1.544164 -0.278302 -0.910734 -1.471865 2013-01-04 -1.194200 -1.295544 -0.522822 0.416136


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# select by label: loc 通过 x y 的label 进行选择输出
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print(df.loc['20130102'])
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print(df.loc[:,['A','B']])
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print(df.loc['20130102', ['A','B']])

A -0.247155 B -1.664754 C 2.044796 D -1.647486 Name: 2013-01-02 00:00:00, dtype: float64 A B 2013-01-01 -0.162028 -1.209992 2013-01-02 -0.247155 -1.664754 2013-01-03 -1.544164 -0.278302 2013-01-04 -1.194200 -1.295544 2013-01-05 2.173732 0.422870 2013-01-06 -0.501831 0.392393 A -0.247155 B -1.664754 Name: 2013-01-02 00:00:00, dtype: float64


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# select by position: iloc 通过位置选择 即通过坐标选择（或范围）
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print(df.iloc[3])
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print(df.iloc[3, 1])
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print(df.iloc[3:5,0:2])
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print(df.iloc[[1,2,4],[0,2]])

A -1.194200 B -1.295544 C -0.522822 D 0.416136 Name: 2013-01-04 00:00:00, dtype: float64 -1.2955436468567092 A B 2013-01-04 -1.194200 -1.295544 2013-01-05 2.173732 0.422870 A C 2013-01-02 -0.247155 2.044796 2013-01-03 -1.544164 -0.910734 2013-01-05 2.173732 -0.665288


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# mixed selection: ix  混合选择 即通过label 和坐标 进行选择
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print(df.ix[:3, ['A', 'C']])
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# Boolean indexing     逻辑选择 可以加入一些逻辑判断进行选择
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print(df[df.A > 0])

A C 2013-01-01 -0.162028 -1.259382 2013-01-02 -0.247155 2.044796 2013-01-03 -1.544164 -0.910734 A B C D 2013-01-05 2.173732 0.42287 -0.665288 -0.585846

设置value


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#与上述差不多 还是确定要改变的数据位置即可
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df.iloc[2,2] = 1111# 通过坐标位置改变
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df.loc['2013-01-03', 'D'] = 2222 #通过标签确定位置 改变
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df.A[df.A>0] = 0 #通过逻辑判断筛选改变
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df['F'] = np.nan #通过标签改变
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df['G']  = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130101', periods=6))# 666
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print(df)


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               A         B            C            D   F  G
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2013-01-01  0.000000 -0.132451     0.021797    -1.656566 NaN  1
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2013-01-02 -0.182227 -0.656873    -0.633688     1.143701 NaN  2
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2013-01-03  0.000000 -0.330042  1111.000000  2222.000000 NaN  3
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2013-01-04 -0.664301  0.248035    -1.286524     0.879787 NaN  4
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2013-01-05 -0.005037 -0.753467    -1.390868     0.842121 NaN  5
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2013-01-06 -0.529367 -1.093037    -0.984761    -1.243129 NaN  6

去除填充判断Nan


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dates = pd.date_range('20130101', periods=6)
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df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A', 'B', 'C', 'D'])
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# 先生成几个nan
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df.iloc[0,1] = np.nan
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df.iloc[1,2] = np.nan
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df.iloc[2,:] = np.nan
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print(df)


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A     B     C     D
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2013-01-01   0.0   NaN   2.0   3.0
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2013-01-02   4.0   5.0   NaN   7.0
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2013-01-03   NaN   NaN   NaN   NaN
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2013-01-04  12.0  13.0  14.0  15.0
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2013-01-05  16.0  17.0  18.0  19.0
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2013-01-06  20.0  21.0  22.0  23.0


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print(df.dropna(axis=0, how='any'))   # how={'any', 'all'} 删除任何有nan的行 （axis=1则为列）


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A     B     C     D
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2013-01-04  12.0  13.0  14.0  15.0
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2013-01-05  16.0  17.0  18.0  19.0
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2013-01-06  20.0  21.0  22.0  23.0


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print(df.dropna(axis=0, how='all'))   # how={'any', 'all'} 删除全为nan的行 （axis=1则为列）


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A     B     C     D
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2013-01-01   0.0   NaN   2.0   3.0
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2013-01-02   4.0   5.0   NaN   7.0
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2013-01-04  12.0  13.0  14.0  15.0
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2013-01-05  16.0  17.0  18.0  19.0
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2013-01-06  20.0  21.0  22.0  23.0


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print(df.fillna(value=0)) #用其它值 填充 nan


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A     B     C     D
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2013-01-01   0.0   0.0   2.0   3.0
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2013-01-02   4.0   5.0   0.0   7.0
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2013-01-03   0.0   0.0   0.0   0.0
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2013-01-04  12.0  13.0  14.0  15.0
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2013-01-05  16.0  17.0  18.0  19.0
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2013-01-06  20.0  21.0  22.0  23.0


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print(pd.isnull(df))  #判断是不是non


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A      B      C      D
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2013-01-01  False   True  False  False
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2013-01-02  False  False   True  False
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2013-01-03   True   True   True   True
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2013-01-04  False  False  False  False
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2013-01-05  False  False  False  False
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2013-01-06  False  False  False  False

合并数据 concat

1573137909760

1573137716364

1573138443149

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1573139671938

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融合数据 merge

可根据columns列合并和根据index索引合并

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绘出数据plot

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读取保存数据

1573183094842

小心得

数据的index可以不写默认为0 1 2 3....，但是列columns一定要写(写成字典的形式)

numpy

matplotlib

基本操作


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import matplotlib.pyplot as plt
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import numpy as np
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#基本用法
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x = np.linspace(-5, 5, 50)
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y = 2*x + 1
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# y = x**2
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plt.plot(x, y)
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plt.show()

1573205858029

使用figure 自定义窗口以及在一个图上画多条曲线


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x = np.linspace(-3, 3, 50)
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y1 = 2*x + 1
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y2 = x**2
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##使用figure 来自定义自己窗口
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#第一个figure figure下面的图都在该figure上绘出  直到定义下一个figure
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plt.figure()
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plt.plot(x, y1)
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#第二个figure num是图片的的索引
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plt.figure(num=3, figsize=(8, 5),)
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plt.plot(x, y2)#第一条曲线
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plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')#第二条曲线
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plt.show()

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设置坐标轴

修改坐标轴的label


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x = np.linspace(-3, 3, 50)
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y1 = 2*x + 1
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y2 = x**2
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plt.figure()
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plt.plot(x, y2)
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# plot the second curve in this figure with certain parameters
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plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
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# set x limits 设置x y的范围
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plt.xlim((-1, 2))
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plt.ylim((-2, 3))
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plt.xlabel('I am x')# x y 的label
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plt.ylabel('I am y')
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# set new sticks #设置新的刻度
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new_ticks = np.linspace(-1, 2, 5)
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print(new_ticks)
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plt.xticks(new_ticks)
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# to use '$ $' for math text and nice looking, e.g. '$\pi$'
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# set tick labels 设置新的label  $是为了让字体更加美观  |为转义字符  可以将alpha转为数学里面的α
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plt.yticks([-2, -1.8, -1, 1.22, 3], 
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           [r'$reallybad$', r'$bad\alpha$', r'$normal$', r'$good$', r'$really\ good$'])
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plt.show()


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[-1.   -0.25  0.5   1.25  2.  ]

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修改坐标轴的位置


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x = np.linspace(-3, 3, 50)
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y2 = x**2
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plt.figure(num=3, figsize=(8, 5),)
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plt.plot(x, y2)#第一条曲线
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plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')#第二条曲线
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# gca = 'get current axis'
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ax= plt.gca()
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ax.spines['right'].set_color('none')# 将上边和右边的边框去掉
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ax.spines['top'].set_color('none')
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plt.show()

png


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x = np.linspace(-3, 3, 50)
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y2 = x**2
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plt.figure(num=3, figsize=(8, 5),)
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plt.plot(x, y2)#第一条曲线
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plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')#第二条曲线
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ax= plt.gca()
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ax.spines['right'].set_color('none')# 将上边和右边的边框去掉
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')# 把下边的那条变设置为x_axis
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# ACCEPTS: [ 'top' | 'bottom' | 'both' | 'default' | 'none' ]
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ax.spines['bottom'].set_position(('data', 0))# 把bottom边放在y==0的高度
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# the 1st is in 'outward' | 'axes' | 'data'
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# axes: percentage of y axis
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# data: depend on y data
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ax.yaxis.set_ticks_position('left') #把左边的那条边设为 y_axis
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# ACCEPTS: [ 'left' | 'right' | 'both' | 'default' | 'none' ]
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ax.spines['left'].set_position(('data',0))# 把left 放在x==0的位置
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plt.show()

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legend 图例


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x = np.linspace(-3, 3, 50)
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y1 = 2*x + 1
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y2 = x**2
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plt.figure()
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# set x limits
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plt.xlim((-1, 2))
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plt.ylim((-2, 3))
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# set new sticks
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new_sticks = np.linspace(-1, 2, 5)
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plt.xticks(new_sticks)
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# set tick labels
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plt.yticks([-2, -1.8, -1, 1.22, 3],
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           [r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
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# 设置图例
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l1, = plt.plot(x, y1, label='linear line')
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l2, = plt.plot(x, y2, color='red', linewidth=1.0, linestyle='--', label='square line')
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#设置图例的位置
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plt.legend(loc='upper right') 
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#plt.legend(handles=[l1, l2], labels=['up', 'down'],  loc='best')
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# the "," is very important in here l1, = plt... and l2, = plt... for this step
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"""legend( handles=(line1, line2, line3),
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           labels=('label1', 'label2', 'label3'),
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           'upper right')
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    The *loc* location codes are::
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          'best' : 0,          (currently not supported for figure legends)
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          'upper right'  : 1,
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          'upper left'   : 2,
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          'lower left'   : 3,
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          'lower right'  : 4,
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          'right'        : 5,
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          'center left'  : 6,
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          'center right' : 7,
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          'lower center' : 8,
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          'upper center' : 9,
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          'center'       : 10,"""
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plt.show()

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添加注解Annotation


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x = np.linspace(-3, 3, 50)
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y = 2*x + 1
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plt.figure(num=1, figsize=(8, 5),)
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plt.plot(x, y,)
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ax = plt.gca()
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ax.spines['right'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')
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ax.spines['bottom'].set_position(('data', 0))
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ax.yaxis.set_ticks_position('left')
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ax.spines['left'].set_position(('data', 0))
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x0 = 1
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y0 = 2*x0 + 1
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plt.plot([x0, x0,], [0, y0,], 'k--', linewidth=2.5)# 画黑色的虚线 k--表示黑色
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plt.scatter([x0, ], [y0, ], s=50, color='b')# 画出该点 b 蓝色
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# method 1:
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#####################
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plt.annotate(r'$2x+1=%s$' % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30),
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             textcoords='offset points', fontsize=16,
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             arrowprops=dict(arrowstyle='->', connectionstyle="arc3,rad=.2"))
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# method 2:
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########################
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plt.text(-3.7, 3, r'$This\ is\ the\ some\ text. \mu\ \sigma_i\ \alpha_t$',
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         fontdict={'size': 16, 'color': 'r'})
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plt.show()

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tick能见度


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x = np.linspace(-3, 3, 50)
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y = 0.1*x
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plt.figure()
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plt.plot(x, y, linewidth=10, zorder=1)      # set zorder for ordering the plot in plt 2.0.2 or higher
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plt.ylim(-2, 2)
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ax = plt.gca() #获得当前轴
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ax.spines['right'].set_color('none')
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ax.spines['top'].set_color('none')
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ax.xaxis.set_ticks_position('bottom')
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ax.spines['bottom'].set_position(('data', 0))
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ax.yaxis.set_ticks_position('left')
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ax.spines['left'].set_position(('data', 0))
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for label in ax.get_xticklabels() + ax.get_yticklabels():
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    label.set_fontsize(12)
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    # set zorder for ordering the plot in plt 2.0.2 or higher
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    label.set_bbox(dict(facecolor='white', edgecolor='none', alpha=0.8, zorder=2))
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plt.show()

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Scatter 散点图


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n = 1024    # data size
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X = np.random.normal(0, 1, n)
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Y = np.random.normal(0, 1, n)
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T = np.arctan2(Y, X)    # for color later on
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plt.scatter(X, Y, s=75, c=T, alpha=.5)# s为size  c为color alpha 为透明度
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plt.xlim(-1.5, 1.5)
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plt.xticks(())  # ignore xticks
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plt.ylim(-1.5, 1.5)
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plt.yticks(())  # ignore yticks 隐藏所有的ticks
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plt.show()

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Bar 柱状图


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n = 12
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X = np.arange(n)#生成从0-n 12个整数
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Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)#产生n个0.5-1的数
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Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
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plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
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plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
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for x, y in zip(X, Y1):
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    # ha: horizontal alignment
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    # va: vertical alignment
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    plt.text(x + 0.1, y + 0.05, '%.2f' % y, ha='center', va='bottom')
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for x, y in zip(X, Y2):
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    # ha: horizontal alignment
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    # va: vertical alignment
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    plt.text(x + 0.4, -y - 0.05, '%.2f' % y, ha='center', va='top')
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plt.xlim(-.5, n)
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plt.xticks(())
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plt.ylim(-1.25, 1.25)
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plt.yticks(())
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plt.show()

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Contours 等高线图


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def f(x,y):
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    # the height function
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    return (1 - x / 2 + x**5 + y**3) * np.exp(-x**2 -y**2)
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n = 256
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x = np.linspace(-3, 3, n)
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y = np.linspace(-3, 3, n)
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X,Y = np.meshgrid(x, y)#网格图
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# 1. use plt.contourf to filling contours 填充
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# X, Y and value for (X,Y) point
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plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap=plt.cm.hot)# 类型 冷热图
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# 2. use plt.contour to add contour lines 画线
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C = plt.contour(X, Y, f(X, Y), 8, colors='black')#8 代表等高线条数 即等高线的稀疏
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# 3. adding label
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plt.clabel(C, inline=True, fontsize=10)# inline 为Flase时 label将被线挡住
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plt.xticks(())
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plt.yticks(())
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plt.show()

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image 图片


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# image data
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a = np.array([0.313660827978, 0.365348418405, 0.423733120134,
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              0.365348418405, 0.439599930621, 0.525083754405,
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              0.423733120134, 0.525083754405, 0.651536351379]).reshape(3,3)
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"""
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for the value of "interpolation", check this:
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http://matplotlib.org/examples/images_contours_and_fields/interpolation_methods.html
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for the value of "origin"= ['upper', 'lower'], check this:
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http://matplotlib.org/examples/pylab_examples/image_origin.html
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"""
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plt.imshow(a, interpolation='nearest', cmap='bone', origin='lower')#
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plt.colorbar(shrink=.92)# 压缩为92%
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plt.xticks(())# 隐藏 ticks 
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plt.yticks(())
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plt.show()

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3D数据


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from mpl_toolkits.mplot3d import Axes3D
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fig = plt.figure()# 建一个图片的窗口
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ax = Axes3D(fig)# 给窗口添加3D维度
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# X, Y value
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X = np.arange(-4, 4, 0.25)
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Y = np.arange(-4, 4, 0.25)
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X, Y = np.meshgrid(X, Y)# 作为网格 
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R = np.sqrt(X ** 2 + Y ** 2)
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# height value
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Z = np.sin(R)
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ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('rainbow'))# 1 1分别为行跨 列跨
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"""
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============= ================================================
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        Argument      Description
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        ============= ================================================
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        *X*, *Y*, *Z* Data values as 2D arrays
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        *rstride*     Array row stride (step size), defaults to 10 
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        *cstride*     Array column stride (step size), defaults to 10
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        *color*       Color of the surface patches
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        *cmap*        A colormap for the surface patches.
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        *facecolors*  Face colors for the individual patches
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        *norm*        An instance of Normalize to map values to colors
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        *vmin*        Minimum value to map
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        *vmax*        Maximum value to map
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        *shade*       Whether to shade the facecolors
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        ============= ================================================
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"""
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# I think this is different from plt12_contours
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ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.get_cmap('rainbow'))
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"""
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==========  ================================================
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        Argument    Description
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        ==========  ================================================
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        *X*, *Y*,   Data values as numpy.arrays
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        *Z*
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        *zdir*      The direction to use: x, y or z (default)
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        *offset*    If specified plot a projection of the filled contour
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                    on this position in plane normal to zdir
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        ==========  ================================================
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"""
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ax.set_zlim(-2, 2)
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plt.show()

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subplot 多合一显示

基本方法


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# example 1:
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###############################
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plt.figure(figsize=(6, 4))
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# plt.subplot(n_rows, n_cols, plot_num)
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plt.subplot(2, 2, 1) #两行两列 第一张图
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plt.plot([0, 1], [0, 1])
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plt.subplot(222)#也可将逗号省掉 两行两列 第二张图
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plt.plot([0, 1], [0, 2])
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plt.subplot(223)#两行两列 第三张图
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plt.plot([0, 1], [0, 3])
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plt.subplot(224)#两行两列 第四张图
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plt.plot([0, 1], [0, 4])
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plt.tight_layout()

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# example 2:
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###############################
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plt.figure(figsize=(6, 4))
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# plt.subplot(n_rows, n_cols, plot_num)
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plt.subplot(2, 1, 1)
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# figure splits into 2 rows, 1 col, plot to the 1st sub-fig
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plt.plot([0, 1], [0, 1])
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plt.subplot(234)
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# figure splits into 2 rows, 3 col, plot to the 4th sub-fig
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plt.plot([0, 1], [0, 2])
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plt.subplot(235)
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# figure splits into 2 rows, 3 col, plot to the 5th sub-fig
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plt.plot([0, 1], [0, 3])
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plt.subplot(236)
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# figure splits into 2 rows, 3 col, plot to the 6th sub-fig
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plt.plot([0, 1], [0, 4])
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plt.tight_layout()
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plt.show()

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其他方法


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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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# method 1: subplot2grid
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##########################
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plt.figure()
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# stands for axes 三行三列 第一图从（0,0）开始 跨度为3（默认为1）
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ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)  
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ax1.plot([1, 2], [1, 2])
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ax1.set_title('ax1_title')# 设置title 的label
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ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2)# 从（1,0）开始 列跨度为2
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ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)#从（1,2）开始 行跨度为2
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ax4 = plt.subplot2grid((3, 3), (2, 0))# 不写跨度 默认 1 1
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ax4.scatter([1, 2], [2, 2])
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ax4.set_xlabel('ax4_x')
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ax4.set_ylabel('ax4_y')
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ax5 = plt.subplot2grid((3, 3), (2, 1))
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# method 2: gridspec
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#########################
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plt.figure()
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gs = gridspec.GridSpec(3, 3)
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# use index from 0
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ax6 = plt.subplot(gs[0, :])
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ax7 = plt.subplot(gs[1, :2])
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ax8 = plt.subplot(gs[1:, 2])
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ax9 = plt.subplot(gs[-1, 0])
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ax10 = plt.subplot(gs[-1, -2])
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# method 3: easy to define structure
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####################################
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f, ((ax11, ax12), (ax13, ax14)) = plt.subplots(2, 2, sharex=True, sharey=True)
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ax11.scatter([1,2], [1,2])
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plt.tight_layout()
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plt.show()

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图中图


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fig = plt.figure()
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x = [1, 2, 3, 4, 5, 6, 7]
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y = [1, 3, 4, 2, 5, 8, 6]
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# below are all percentage
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left, bottom, width, height = 0.1, 0.1, 0.8, 0.8# 小图相对于figure 的位置大小 （百分比）
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ax1 = fig.add_axes([left, bottom, width, height])  # main axes 第一个图
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ax1.plot(x, y, 'r')
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ax1.set_xlabel('x')
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ax1.set_ylabel('y')
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ax1.set_title('title')
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ax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25])  # inside axes 第二个小图
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ax2.plot(y, x, 'b')
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ax2.set_xlabel('x')
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ax2.set_ylabel('y')
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ax2.set_title('title inside 1')
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# different method to add axes
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####################################
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plt.axes([0.6, 0.2, 0.25, 0.25])
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plt.plot(y[::-1], x, 'g')
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plt.xlabel('x')
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plt.ylabel('y')
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plt.title('title inside 2')
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plt.show()

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次坐标轴


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x = np.arange(0, 10, 0.1)
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y1 = 0.05 * x**2
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y2 = -1 *y1
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fig, ax1 = plt.subplots()
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ax2 = ax1.twinx()    # mirror the ax1 把ax1 的数据镜像
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ax1.plot(x, y1, 'g-')
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ax2.plot(x, y2, 'b-')
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ax1.set_xlabel('X data')
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ax1.set_ylabel('Y1 data', color='g')
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ax2.set_ylabel('Y2 data', color='b')
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plt.show()
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动画


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from matplotlib import animation
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fig, ax = plt.subplots()
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x = np.arange(0, 2*np.pi, 0.01)
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line, = ax.plot(x, np.sin(x))
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def animate(i):
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    line.set_ydata(np.sin(x + i/10.0))  # update the data
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    return line,
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# Init only required for blitting to give a clean slate.
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def init():
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    line.set_ydata(np.sin(x))
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    return line,
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# call the animator.  blit=True means only re-draw the parts that have changed.
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# blit=True dose not work on Mac, set blit=False
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# interval= update frequency
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ani = animation.FuncAnimation(fig=fig, func=animate, frames=100, init_func=init,
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                              interval=20, blit=False)
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# save the animation as an mp4.  This requires ffmpeg or mencoder to be
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# installed.  The extra_args ensure that the x264 codec is used, so that
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# the video can be embedded in html5.  You may need to adjust this for
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# your system: for more information, see
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# http://matplotlib.sourceforge.net/api/animation_api.html
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# anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
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plt.show()

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