# -*- coding: utf-8 -*-
# @File    : pandas_demo.py
# @Date    : 2018-05-20
# pandas数据类型的算术运算
import pandas as pd
import numpy as np
# 数据准备
a = pd.DataFrame(np.arange(12).reshape(3, 4))
print(a)
"""
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
"""
b = pd.DataFrame(np.arange(20).reshape(4, 5))
print(b)
"""
    0   1   2   3   4
0   0   1   2   3   4
1   5   6   7   8   9
2  10  11  12  13  14
3  15  16  17  18  19
"""
# 算术运算
# 自动补齐，缺项补NaN
c = a + b
print(c)
"""
      0     1     2     3   4
0   0.0   2.0   4.0   6.0 NaN
1   9.0  11.0  13.0  15.0 NaN
2  18.0  20.0  22.0  24.0 NaN
3   NaN   NaN   NaN   NaN NaN
"""
d = a * b
print(d)
"""
      0     1      2      3   4
0   0.0   1.0    4.0    9.0 NaN
1  20.0  30.0   42.0   56.0 NaN
2  80.0  99.0  120.0  143.0 NaN
3   NaN   NaN    NaN    NaN NaN
"""
# fill_value参数替代NaN，替代后参与运算
e = a.add(b, fill_value=10)
print(e)
"""
      0     1     2     3     4
0   0.0   2.0   4.0   6.0  14.0
1   9.0  11.0  13.0  15.0  19.0
2  18.0  20.0  22.0  24.0  24.0
3  25.0  26.0  27.0  28.0  29.0
"""
f = a.mul(b, fill_value=10)
print(f)
"""
       0      1      2      3      4
0    0.0    1.0    4.0    9.0   40.0
1   20.0   30.0   42.0   56.0   90.0
2   80.0   99.0  120.0  143.0  140.0
3  150.0  160.0  170.0  180.0  190.0
"""
# 不同维度间为广播运算，一维Series默认在轴1参与运算
s = pd.Series(np.arange(10, 15, 1))
print(s)
"""
0    10
1    11
2    12
3    13
4    14
dtype: int32
"""
s1 = s -10
print(s1)
"""
0    0
1    1
2    2
3    3
4    4
dtype: int32
"""
b2 = b - s
print(b2)
"""
    0   1   2   3   4
0 -10 -10 -10 -10 -10
1  -5  -5  -5  -5  -5
2   0   0   0   0   0
3   5   5   5   5   5
"""
# 使用运算方法可以令一维Series参与轴0运算
s = pd.Series(np.arange(4))
print(s)
"""
0    0
1    1
2    2
3    3
dtype: int32
"""
b3 = b.sub(s, axis=0)
print(b3)
"""
    0   1   2   3   4
0   0   1   2   3   4
1   4   5   6   7   8
2   8   9  10  11  12
3  12  13  14  15  16
"""
# 比较运算
a = pd.DataFrame(np.arange(12).reshape(3, 4))
print(a)
"""
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
"""
b = pd.DataFrame(np.arange(12, 0, -1).reshape(3, 4))
print(b)
"""
    0   1   2  3
0  12  11  10  9
1   8   7   6  5
2   4   3   2  1
"""
# 同维度运算，尺寸一致
c = a > b
print(c)
"""
       0      1      2      3
0  False  False  False  False
1  False  False  False   True
2   True   True   True   True
"""
d = a == b
print(d)
"""
       0      1      2      3
0  False  False  False  False
1  False  False   True  False
2  False  False  False  False
"""
# 不同维度，广播运算，默认在1轴
s = pd.Series(np.arange(4))
print(s)
"""
0    0
1    1
2    2
3    3
dtype: int32
"""
print(a > s)
"""
       0      1      2      3
0  False  False  False  False
1   True   True   True   True
2   True   True   True   True
"""
print(s > 0)
"""
0    False
1     True
2     True
3     True
dtype: bool
"""