Numpy & Pandas & Matplotilb 部分 API 操作
Numpy
导入
import numpy as np
API
创建数组
np.array([10, 11, 12, 13])# [10 11 12 13]
np.array([10, 11, 12, 13, 14 ,15]).reshape([2,3])# [# [10 11 12]# [13 14 15]# ]
np.array([[1, 2], [3, 4]])# [# [1 2]# [3 4]# ]
np.arange(4)# [0 1 2 3]
np.arange(2, 6)# [2 3 4 5]
np.arange(4).reshape([2,2])# [# [0 1]# [2 3]# ]
np.random.random([2,3])# [# [ 0.00136044 0.46854718 0.59149907]# [ 0.75636339 0.18204628 0.53191402]# ]求值
arr = np.array([10, 11, 12, 13, 14 ,15]).reshape([2,3])# [# [10 11 12]# [13 14 15]# ]
# 总数np.sum(arr, axis=0)# [23 25 27]np.sum(arr, axis=1)# [33 42]
# 最小数np.min(arr, axis=0)# [10 11 12]np.min(arr, axis=1)# [10 13]
# 最大数np.max(arr, axis=0)# [13 14 15]np.max(arr, axis=1)# [12 15]
# 最大/小值得索引值np.argmin(arr)# 0 0 是索引np.argmax(arr)# 5 5 是索引
# 平均值arr.mean()# np.mean(arr)# 12.5np.average(arr)# 12.5
# 逐步增加np.cumsum(arr)# [10 21 33 46 60 75]
# 相差np.diff(arr)# [# [1 1]# [1 1]# ]
# 替换np.clip(arr, 11, 14)# [# [11 11 12]# [13 14 14]# ]# 小于 11 的数替换成 11，大于 14 的数替换成 14，其他数不变索引
arr = np.arange(3, 15).reshape([3,4])# [# [ 3 4 5 6]# [ 7 8 9 10]# [11 12 13 14]# ]
arr[1, 1]# arr[1][1]# 8
arr[:, 1]# [ 4 8 12]
arr[1, :]# [ 7 8 9 10]
arr[1, 1:3]# [8 9]
arr.flatten()# [ 3 4 5 6 7 8 9 10 11 12 13 14]
for i in arr.flat: print(i)# 每行打印出值。arr.flat 是迭代器合并
A = np.array([1, 1, 1])B = np.array([2, 2, 2])
np.vstack((A, B))# [# [1 1 1]# [2 2 2]# ]np.hstack((A, B))# [1 1 1 2 2 2]分割
arr = np.arange(12).reshape([3,4])# [# [ 0 1 2 3]# [ 4 5 6 7]# [ 8 9 10 11]# ]
np.split(arr, 2, axis=1)# [array([# [0, 1],# [4, 5],# [8, 9]# ]),# array([# [ 2, 3],# [ 6, 7],# [10, 11]]# )]Pandas
导入
import pandas as pd
API
创建列表
pd.Series([1, 3, 6, np.nan, 44, 1])# 0 1.0# 1 3.0# 2 6.0# 3 NaN# 4 44.0# 5 1.0# dtype: float64
pd.date_range('20171108', periods=6)# DatetimeIndex(# ['2017-11-08', '2017-11-09', '2017-11-10', '2017-11-11','2017-11-12', '2017-11-13'],# dtype='datetime64[ns]',# freq='D'# )
dates = pd.date_range('20171108', periods=6)pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['a', 'b', 'c', 'd'])# a b c d# 2017-11-08 0.644350 1.122020 -1.263401 0.163371# 2017-11-09 0.573329 -0.242054 -0.342220 1.070905# 2017-11-10 0.714291 -0.721509 -2.298672 -0.513572# 2017-11-11 -0.614927 2.010482 -1.369179 -0.901276# 2017-11-12 0.709672 -0.430620 1.070244 -2.308874# 2017-11-13 1.284080 1.169807 1.668942 0.859300
pd.DataFrame({ 'A': 1., 'B': pd.Timestamp('20171108'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(['test', 'train', 'test', 'train']), 'F': 'foo'})# A B C D E F# 0 1.0 2017-11-08 1.0 3 test foo# 1 1.0 2017-11-08 1.0 3 train foo# 2 1.0 2017-11-08 1.0 3 test foo# 3 1.0 2017-11-08 1.0 3 train foo选择获取
datas = pd.DataFrame({ 'A': 1., 'B': pd.Timestamp('20171108'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(['test', 'train', 'test', 'train']), 'F': 'foo'})# A B C D E F# 0 1.0 2017-11-08 1.0 3 test foo# 1 1.0 2017-11-08 1.0 3 train foo# 2 1.0 2017-11-08 1.0 3 test foo# 3 1.0 2017-11-08 1.0 3 train foo
datas.A# datas['A']# 0 1.0# 1 1.0# 2 1.0# 3 1.0# Name: A, dtype: float64
datas[0:3]# A B C D E F# 0 1.0 2017-11-08 1.0 3 test foo# 1 1.0 2017-11-08 1.0 3 train foo# 2 1.0 2017-11-08 1.0 3 test foo
datas.loc[0]# 当 index 是类似'2017-11-8 的时候'，datas.loc['20171108']# A 1# B 2017-11-08 00:00:00# C 1# D 3# E test# F foo# Name: 0, dtype: object
datas.loc[:,['A', 'B']]# A B# 0 1.0 2017-11-08# 1 1.0 2017-11-08# 2 1.0 2017-11-08# 3 1.0 2017-11-08
datas.loc[[1, 3],['A', 'B']]# A B# 1 1.0 2017-11-08# 3 1.0 2017-11-08
# icol 是基于行号获取的，col 是基于 index 获取的，ix 是他们俩的混合 (index、行号都可以)# icol[1]# ix[1]# 当 index 为 2017-11-08 时，ix['20171108']
datas[datas.E == 'test']# A B C D E F# 2017-11-08 1.0 2017-11-08 1.0 3 test foo# 2017-11-10 1.0 2017-11-08 1.0 3 test foo
datas.index# Int64Index([0, 1, 2, 3], dtype='int64')
datas.columns# Index([u'A', u'B', u'C', u'D', u'E', u'F'], dtype='object')
datas.values# array(# [# [1.0, Timestamp('2017-11-08 00:00:00'), 1.0, 3, 'test', 'foo'],# [1.0, Timestamp('2017-11-08 00:00:00'), 1.0, 3, 'train', 'foo'],# [1.0, Timestamp('2017-11-08 00:00:00'), 1.0, 3, 'test', 'foo'],# [1.0, Timestamp('2017-11-08 00:00:00'), 1.0, 3, 'train', 'foo']# ],# dtype=object)排序
datas.sort_index(axis=0, ascending=False)# F E D C B A# 0 foo test 3 1.0 2017-11-08 1.0# 1 foo train 3 1.0 2017-11-08 1.0# 2 foo test 3 1.0 2017-11-08 1.0# 3 foo train 3 1.0 2017-11-08 1.0
datas.sort_index(axis=0, ascending=False)# A B C D E F# 3 1.0 2017-11-08 1.0 3 train foo# 2 1.0 2017-11-08 1.0 3 test foo# 1 1.0 2017-11-08 1.0 3 train foo# 0 1.0 2017-11-08 1.0 3 test foo
datas.sort_values(by='E')# A B C D E F# 0 1.0 2017-11-08 1.0 3 test foo# 2 1.0 2017-11-08 1.0 3 test foo# 1 1.0 2017-11-08 1.0 3 train foo# 3 1.0 2017-11-08 1.0 3 train foo设置值
datas = pd.DataFrame({ 'A': pd.Series([1, 5, 'test', 'foo'], index=list(range(4))), 'B': pd.Series([np.nan, 1, np.nan, 'test'], index=list(range(4))), 'C': pd.Series(1, index=list(range(4)), dtype='float32'),})# A B C# 0 1 NaN 1.0# 1 5 1 1.0# 2 test NaN 1.0# 3 foo test 1.0
datas.dropna(axis=0, how='any')# 当 axis 是 1 时，则判断竖向里是否含有 NaN 的值# how = 'any' || 'all' 默认是 any# 当是 any 的时候，有一个值是 NaN 的时，就删除这一行。# 当时 all 的时候，这一行全部为 NaN 时，就删除这一行# A B C# 1 5 1 1.0# 3 foo test 1.0
datas.fillna(value=0)# A B C# 0 1 0 1.0# 1 5 1 1.0# 2 test 0 1.0# 3 foo test 1.0
datas.isnull()# A B C# 0 False True False# 1 False False False# 2 False True False# 3 False False False
# 当数据特别大的时候，或者只想判断是否有值是 NaN 的值时# np.any(datas.isnull()) == True# 当有值时 NaN 时，将返回 True导入导出
pd.read_csv('***.csv',delimiter=',',encoding='utf-8',names=['test1','test2','test3'])# 参数一: 读取的目标文件# 参数二: csv 文件的分隔符# 参数三: 编码# 参数四: 设置列名
# test1 test2 test3# 0 2017-11-18 ABC 51315.0# 1 2017-11-19 DEF 5659.0# 2 2017-11-20 GHI 1599.0# 3 2017-11-21 JKL 2224.0
datas.to_csv('**.csv')
合并
concat
datas1 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['a', 'b', 'c', 'd'])# a b c d# 0 0.0 0.0 0.0 0.0# 1 0.0 0.0 0.0 0.0# 2 0.0 0.0 0.0 0.0
datas2 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['a', 'b', 'c', 'd'])# a b c d# 0 1.0 1.0 1.0 1.0# 1 1.0 1.0 1.0 1.0# 2 1.0 1.0 1.0 1.0
datas3 = pd.DataFrame(np.ones((3, 4)) * 2, columns=['a', 'b', 'c', 'd'])# a b c d# 0 2.0 2.0 2.0 2.0# 1 2.0 2.0 2.0 2.0# 2 2.0 2.0 2.0 2.0
pd.concat([datas1, datas2, datas3], axis=0, ignore_index=True)# a b c d# 0 0.0 0.0 0.0 0.0# 1 0.0 0.0 0.0 0.0# 2 0.0 0.0 0.0 0.0# 3 1.0 1.0 1.0 1.0# 4 1.0 1.0 1.0 1.0# 5 1.0 1.0 1.0 1.0# 6 2.0 2.0 2.0 2.0# 7 2.0 2.0 2.0 2.0# 8 2.0 2.0 2.0 2.0
pd.concat([datas1, datas2, datas3], axis=1)# a b c d a b c d a b c d# 0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0# 1 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0# 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0concat 部分参数
在 concat 里，join 的默认参数时 outer
datas1 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['a', 'b', 'c', 'd'], index=[1, 2, 3])# a b c d# 1 0.0 0.0 0.0 0.0# 2 0.0 0.0 0.0 0.0# 3 0.0 0.0 0.0 0.0
datas2 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['b', 'c', 'd', 'e'], index=[2, 3, 4])# b c d e# 2 1.0 1.0 1.0 1.0# 3 1.0 1.0 1.0 1.0# 4 1.0 1.0 1.0 1.0
pd.concat([datas1, datas2], join='outer')# a b c d e# 1 0.0 0.0 0.0 0.0 NaN# 2 0.0 0.0 0.0 0.0 NaN# 3 0.0 0.0 0.0 0.0 NaN# 2 NaN 1.0 1.0 1.0 1.0# 3 NaN 1.0 1.0 1.0 1.0# 4 NaN 1.0 1.0 1.0 1.0
pd.concat([datas1, datas2], join='inner')# b c d# 1 0.0 0.0 0.0# 2 0.0 0.0 0.0# 3 0.0 0.0 0.0# 2 1.0 1.0 1.0# 3 1.0 1.0 1.0# 4 1.0 1.0 1.0
pd.concat([datas1, datas2], axis=1, join_axes=[datas2.index])# a b c d b c d e# 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0# 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0# 4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0# 如果没有 join_axes 值时:# a b c d b c d e# 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN# 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0# 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0# 4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0append
datas1 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['a', 'b', 'c', 'd'])# a b c d# 0 0.0 0.0 0.0 0.0# 1 0.0 0.0 0.0 0.0# 2 0.0 0.0 0.0 0.0
datas2 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])# a 1# b 2# c 3# d 4# dtype: int64
datas1.append(datas2, ignore_index=True)# a b c d# 0 0.0 0.0 0.0 0.0# 1 0.0 0.0 0.0 0.0# 2 0.0 0.0 0.0 0.0# 3 1.0 2.0 3.0 4.0merge
left = pd.DataFrame({ 'key': ['k0', 'k1', 'k2', 'k3'], 'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']})# A B key# 0 A0 B0 k0# 1 A1 B1 k1# 2 A2 B2 k2# 3 A3 B3 k3
right = pd.DataFrame({ 'key': ['k0', 'k1', 'k2', 'k3'], 'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']})# C D key# 0 C0 D0 k0# 1 C1 D1 k1# 2 C2 D2 k2# 3 C3 D3 k3
pd.merge(left, right, on='key')# A B key C D# 0 A0 B0 k0 C0 D0# 1 A1 B1 k1 C1 D1# 2 A2 B2 k2 C2 D2# 3 A3 B3 k3 C3 D3left = pd.DataFrame({ 'key1': ['k0', 'k0', 'k1', 'k2'], 'key2': ['k0', 'k1', 'k0', 'k1'], 'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']})# A B key1 key2# 0 A0 B0 k0 k0# 1 A1 B1 k0 k1# 2 A2 B2 k1 k0# 3 A3 B3 k2 k1
right = pd.DataFrame({ 'key1': ['k0', 'k1', 'k1', 'k2'], 'key2': ['k0', 'k0', 'k0', 'k0'], 'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']})# C D key1 key2# 0 C0 D0 k0 k0# 1 C1 D1 k1 k0# 2 C2 D2 k1 k0# 3 C3 D3 k2 k0
pd.merge(left, right, on=['key1', 'key2'], how='inner')# how 默认是 inner# A B key1 key2 C D# 0 A0 B0 k0 k0 C0 D0# 1 A2 B2 k1 k0 C1 D1# 2 A2 B2 k1 k0 C2 D2
pd.merge(left, right, on=['key1', 'key2'], how='outer')# A B key1 key2 C D# 0 A0 B0 k0 k0 C0 D0# 1 A1 B1 k0 k1 NaN NaN# 2 A2 B2 k1 k0 C1 D1# 3 A2 B2 k1 k0 C2 D2# 4 A3 B3 k2 k1 NaN NaN# 5 NaN NaN k2 k0 C3 D3
pd.merge(left, right, on=['key1', 'key2']. how='right')# A B key1 key2 C D# 0 A0 B0 k0 k0 C0 D0# 1 A2 B2 k1 k0 C1 D1# 2 A2 B2 k1 k0 C2 D2# 3 NaN NaN k2 k0 C3 D3
pd.merge(left, right, on=['key1', 'key2'], how='left')# A B key1 key2 C D# 0 A0 B0 k0 k0 C0 D0# 1 A1 B1 k0 k1 NaN NaN# 2 A2 B2 k1 k0 C1 D1# 3 A2 B2 k1 k0 C2 D2# 4 A3 B3 k2 k1 NaN NaNmatplotilb
导入
import matplotlib.pyplot as plt
API
plot
data = pd.Series(np.random.randn(1000)) # 随机 1000 个数data = data.cumsum() # 累加# 因为 pandas 本来就是一个数据，所以可以直接 plot，# 还有两种写法: plt.plot(x=，y = ) 或者 plt.plot([xxx，xxx]，[yyy，yyy])data.plot()plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号# linewidth 线条的宽度# linestyle 线条风格 (-实线 --破折线 -。点划线 : 虚线 None 说明都不画)plt.plot([1,50,100],[1,4,9], linewidth=2.5, linestyle='--', label='lalala')plt.legend(loc='upper left') # 没有这句，上面的 label 将不会显示plt.plot([1,100,200],[1,7,9]) # 第三个数据plt.title('Demo') # 标题plt.xlabel('xxx') # x 轴名称plt.ylabel('yyy') # y 轴名称plt.text(60, 10, u'说明文字') # 说明文字plt.show() # 显示
# 随机 1000 行 4 列的数字，行数从 0 到 999，列表为 A B C Ddata = pd.DataFrame(np.random.randn(1000, 4), index=np.arange(1000), columns=list('ABCD'))data = data.cumsum() # 累加data.plot()plt.show()
其他图
柱状图
plt.bar(left, height, width=0.8)散点图
plt.scatter(x,y)