【Python数据分析】数值计算和统计基础

試著忘記壹切 提交于 2020-02-17 23:57:15

1.axis与skipna参数的使用

注意:np.nan表示空值

# 建立数据集
import numpy as np
import pandas as pd

df = pd.DataFrame({'key1':[4,5,6,np.nan,2], # np.nan表示空值
                   'key2':[1,2,np.nan,15,10],
                   'key3':[1,2,3,'m','n'],
                   'key4':['a1','a2','a3','a4','a5']},index = ['a','b','c','d','e']
)
print(df)
print('-----------------分割线-------------------')
# 打印每列数据类型
print(df['key1'].dtype,df['key2'].dtype,df['key3'].dtype,df['key4'].dtype)

# 求整体平均值(只会对数字列进行统计)
print('----------------整体平均------------------')
print(df.mean())

# 根据索引求平均值
print('key2列的平均值:',df['key2'].mean())

print('----------------axis参数------------------')
# 单独统计一列的平均值,axis=0表示以列计算 axis=1表示以行计算,默认axis=0
m1 = df.mean(axis=1) 
print(m1)

print('----------------skipna参数------------------')
# skipna参数:是否忽略NaN,默认True,如False,有NaN的列统计结果仍未NaN
m2 = df.mean(skipna=False) 
print(m2)

输出结果:

2.常用统计方法

其他随机方法列表如下:

  • count:统计非Na值的数量
  • min:统计最小值
  • max:统计最大值
  • quantile:统计分位数,参数q确定位置,例如:quantile(q=0.75)
  • sum:求和
  • median:求中位数
  • std:标准差
  • var:方差
  • skew:偏度
  • kurt:峰度

实战演练

import numpy as np
import pandas as pd

df = pd.DataFrame({'key1':[1,2,3,4,25,6,7,8,9,10],
                  'key2':[10,20,30,300,50,60,70,80,90,100]}) 
print(df)
print('-' * 20)
# 求最大值
print("计数count:")
print(df.count())

print('-' * 20)

print("最小值min:")
print(df.min())

print('-' * 20)

print("最大值min:")
print(df.max())

print('-' * 20)

print("第三分位数quantile:")
print(df.quantile(0.75))

print('-' * 20)

print("求和sum:")
print(df.sum())

print('-' * 20)

print("求中位数median:")
print(df.median())

print('-' * 20)

print("求标准差std:")
print(df.std())

print('-' * 20)

print("求方差var:")
print(df.var())

print('-' * 20)

print("求偏度skew:")
print(df.skew())

print('-' * 20)

print("求峰度kurt:")
print(df.kurt())

输出结果:

3.求累计值:cumsum(累计和),累计积

import numpy as np
import pandas as pd

df = pd.DataFrame({'key1':[1,2,3,4,25,6,7,8,9,10],
                  'key2':[10,20,30,300,50,60,70,80,90,100]}) 
print(df)
print('-------------累计和&累计积-----------------')
df['key1累计和'] = df['key1'].cumsum()
df['key2累计和'] = df['key2'].cumsum()

df['key1累计积'] = df['key1'].cumsum()
df['key2累计积'] = df['key1'].cumsum()
print(df) 

输出结果:

4.唯一值:unique

import pandas as pd
s = pd.Series(list('abdsssdsd'))
print(s)

# 求唯一值
sq = s.unique()
print('-' * 50)
print(sq,type(sq))

输出结果:

5.值计数:value_counts()

值计数主要针对新的Series,计算出不同值出现的频率

import pandas as pd
s = pd.Series(list('abdsssdsd'))
print(s)
print('-' * 50)
print(s.value_counts(sort = False)) # sort参数:排序,默认为True

输出结果:

6.成员资格:isin()

判断某个数据集的元素是否在另外一个数据集中,返回结果为布尔值。

s = pd.Series(np.arange(10,13))
df = pd.DataFrame({'key1':list('asdc'),
                  'key2':np.arange(4,8)})
print(s)
print('-' * 50)
print(df)
print('-' * 50)

print(s.isin([12,15]))
print('-' * 50)
print(df.isin(['a','bc','10',8]))

输出结果:

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