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Pandas-数据操作-数值型（二）：累计统计函数【cumsum、cumprod、cummax、cummin】【计算前1/2/3/…/n个数的和、积、最大值、最小值】_pandas cumsum
作者：不正经 | 2024-03-07 09:16:51
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pandas cumsum
一、累计统计函数
| 函数 | 作用 |
|---|---|
cumsum | 计算前1/2/3/…/n个数的和 |
cummax | 计算前1/2/3/…/n个数的最大值 |
cummin | 计算前1/2/3/…/n个数的最小值 |
cumprod | 计算前1/2/3/…/n个数的积 |
import numpy as np import pandas as pd # np.nan ：空值 df = pd.DataFrame({'key1': np.arange(10), 'key2': np.random.rand(10) * 10}) print("df = \n", df) print('-' * 200) key1_cumsum = df['key1'].cumsum() key2_cumsum = df['key2'].cumsum() print("key1_cumsum = \n{0} \ntype(key1_cumsum) = {1}".format(key1_cumsum, type(key1_cumsum))) print('-' * 50) print("key2_cumsum = \n{0} \ntype(key2_cumsum) = {1}".format(key2_cumsum, type(key2_cumsum))) print('-' * 50) df['key1_cumsum'] = df['key1'].cumsum() df['key2_cumsum'] = df['key2'].cumsum() print("添加cumsum样本的累计和之后: df = \n", df) print('-' * 200) key1_cumprod = df['key1'].cumprod() key2_cumprod = df['key2'].cumprod() print("key1_cumprod = \n{0} \ntype(key1_cumprod) = {1}".format(key1_cumprod, type(key1_cumprod))) print('-' * 50) print("key2_cumprod = \n{0} \ntype(key2_cumprod) = {1}".format(key2_cumprod, type(key2_cumprod))) print('-' * 50) df['key1_cumprod'] = key1_cumprod df['key2_cumprod'] = key2_cumprod print("添加cumprod样本的累计积之后: df = \n", df) print('-' * 200) # cummax,cummin分别求累计最大值，累计最小值，会填充key1，和key2的值，返回新的对象 df1 = df.cummax() df2 = df.cummin() print("df = \n", df) print('-' * 50) print("df1 = df.cummax() = \n", df1) print('-' * 50) print("df2 = df.cummin() = \n", df2) print('-' * 200)
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打印结果：
df = key1 key2 0 0 5.946567 1 1 6.500338 2 2 0.517269 3 3 6.888832 4 4 0.029891 5 5 6.908777 6 6 4.522801 7 7 6.755125 8 8 6.676930 9 9 3.002233 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- key1_cumsum = 0 0 1 1 2 3 3 6 4 10 5 15 6 21 7 28 8 36 9 45 Name: key1, dtype: int32 type(key1_cumsum) = <class 'pandas.core.series.Series'> -------------------------------------------------- key2_cumsum = 0 5.946567 1 12.446905 2 12.964174 3 19.853006 4 19.882897 5 26.791673 6 31.314474 7 38.069599 8 44.746529 9 47.748762 Name: key2, dtype: float64 type(key2_cumsum) = <class 'pandas.core.series.Series'> -------------------------------------------------- 添加cumsum样本的累计和之后: df = key1 key2 key1_cumsum key2_cumsum 0 0 5.946567 0 5.946567 1 1 6.500338 1 12.446905 2 2 0.517269 3 12.964174 3 3 6.888832 6 19.853006 4 4 0.029891 10 19.882897 5 5 6.908777 15 26.791673 6 6 4.522801 21 31.314474 7 7 6.755125 28 38.069599 8 8 6.676930 36 44.746529 9 9 3.002233 45 47.748762 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- key1_cumprod = 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 Name: key1, dtype: int32 type(key1_cumprod) = <class 'pandas.core.series.Series'> -------------------------------------------------- key2_cumprod = 0 5.946567 1 38.654696 2 19.994865 3 137.741271 4 4.117176 5 28.444652 6 128.649488 7 869.043329 8 5802.541623 9 17420.580379 Name: key2, dtype: float64 type(key2_cumprod) = <class 'pandas.core.series.Series'> -------------------------------------------------- 添加cumprod样本的累计积之后: df = key1 key2 key1_cumsum key2_cumsum key1_cumprod key2_cumprod 0 0 5.946567 0 5.946567 0 5.946567 1 1 6.500338 1 12.446905 0 38.654696 2 2 0.517269 3 12.964174 0 19.994865 3 3 6.888832 6 19.853006 0 137.741271 4 4 0.029891 10 19.882897 0 4.117176 5 5 6.908777 15 26.791673 0 28.444652 6 6 4.522801 21 31.314474 0 128.649488 7 7 6.755125 28 38.069599 0 869.043329 8 8 6.676930 36 44.746529 0 5802.541623 9 9 3.002233 45 47.748762 0 17420.580379 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- df = key1 key2 key1_cumsum key2_cumsum key1_cumprod key2_cumprod 0 0 5.946567 0 5.946567 0 5.946567 1 1 6.500338 1 12.446905 0 38.654696 2 2 0.517269 3 12.964174 0 19.994865 3 3 6.888832 6 19.853006 0 137.741271 4 4 0.029891 10 19.882897 0 4.117176 5 5 6.908777 15 26.791673 0 28.444652 6 6 4.522801 21 31.314474 0 128.649488 7 7 6.755125 28 38.069599 0 869.043329 8 8 6.676930 36 44.746529 0 5802.541623 9 9 3.002233 45 47.748762 0 17420.580379 -------------------------------------------------- df1 = df.cummax() = key1 key2 key1_cumsum key2_cumsum key1_cumprod key2_cumprod 0 0 5.946567 0 5.946567 0 5.946567 1 1 6.500338 1 12.446905 0 38.654696 2 2 6.500338 3 12.964174 0 38.654696 3 3 6.888832 6 19.853006 0 137.741271 4 4 6.888832 10 19.882897 0 137.741271 5 5 6.908777 15 26.791673 0 137.741271 6 6 6.908777 21 31.314474 0 137.741271 7 7 6.908777 28 38.069599 0 869.043329 8 8 6.908777 36 44.746529 0 5802.541623 9 9 6.908777 45 47.748762 0 17420.580379 -------------------------------------------------- df2 = df.cummin() = key1 key2 key1_cumsum key2_cumsum key1_cumprod key2_cumprod 0 0 5.946567 0 5.946567 0 5.946567 1 0 5.946567 0 5.946567 0 5.946567 2 0 0.517269 0 5.946567 0 5.946567 3 0 0.517269 0 5.946567 0 5.946567 4 0 0.029891 0 5.946567 0 4.117176 5 0 0.029891 0 5.946567 0 4.117176 6 0 0.029891 0 5.946567 0 4.117176 7 0 0.029891 0 5.946567 0 4.117176 8 0 0.029891 0 5.946567 0 4.117176 9 0 0.029891 0 5.946567 0 4.117176 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Process finished with exit code 0
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二、累计统计函数怎么用？
以上这些函数可以对series和dataframe操作
这里我们按照时间的从前往后来进行累计
- 排序
# 排序之后，进行累计求和 data = data.sort_index()- 1
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- 对p_change进行求和
stock_rise = data['p_change'] # plot方法集成了前面直方图、条形图、饼图、折线图 stock_rise.cumsum() 2015-03-02 2.62 2015-03-03 4.06 2015-03-04 5.63 2015-03-05 7.65 2015-03-06 16.16 2015-03-09 16.37 2015-03-10 18.75 2015-03-11 16.36 2015-03-12 15.03 2015-03-13 17.58 2015-03-16 20.34 2015-03-17 22.42 2015-03-18 23.28 2015-03-19 23.74 2015-03-20 23.48 2015-03-23 23.74
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使用matplotlib画出连续求和的结果：
如果要使用plot函数，需要导入matplotlib.
import matplotlib.pyplot as plt
# plot显示图形
stock_rise.cumsum().plot()
# 需要调用show，才能显示出结果
plt.show()
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