本文实例讲述了Python3.5Pandas模块缺失值处理和层次索引。分享给大家供大家参考，具体如下
本文实例讲述了Python3.5 Pandas模块缺失值处理和层次索引。分享给大家供大家参考，具体如下：
1、pandas缺失值处理
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
df3 = DataFrame([
["Tom",np.nan,456.67,"M"],
["Merry",34,345.56,np.nan],
[np.nan,np.nan,np.nan,np.nan],
["John",23,np.nan,"M"],
["Joe",18,385.12,"F"]
],columns = ["name","age","salary","gender"])
print(df3)
print("=======判断NaN值=======")
print(df3.isnull())
print("=======判断非NaN值=======")
print(df3.notnull())
print("=======删除包含NaN值的行=======")
print(df3.dropna())
print("=======删除全部为NaN值的行=======")
print(df3.dropna(how="all"))
df3.ix[2,0] = "Gerry" #修改第2行第0列的值
print(df3)
print("=======删除包含NaN值的列=======")
print(df3.dropna(axis=1))
运行结果：
name age salary gender 0 Tom NaN 456.67 M 1 Merry 34.0 345.56 NaN 2 NaN NaN NaN NaN 3 John 23.0 NaN M 4 Joe 18.0 385.12 F =======判断NaN值======= name age salary gender 0 False True False False 1 False False False True 2 True True True True 3 False False True False 4 False False False False =======判断非NaN值======= name age salary gender 0 True False True True 1 True True True False 2 False False False False 3 True True False True 4 True True True True =======删除包含NaN值的行======= name age salary gender 4 Joe 18.0 385.12 F =======删除全部为NaN值的行======= name age salary gender 0 Tom NaN 456.67 M 1 Merry 34.0 345.56 NaN 3 John 23.0 NaN M 4 Joe 18.0 385.12 F name age salary gender 0 Tom NaN 456.67 M 1 Merry 34.0 345.56 NaN 2 Gerry NaN NaN NaN 3 John 23.0 NaN M 4 Joe 18.0 385.12 F =======删除包含NaN值的列======= name 0 Tom 1 Merry 2 Gerry 3 John 4 Joe
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
df4 = DataFrame(np.random.randn(7,3))
print(df4)
df4.ix[:4,1] = np.nan #第0至3行，第1列的数据
df4.ix[:2,2] = np.nan
print(df4)
print(df4.fillna(0)) #将缺失值用传入的指定值0替换
print(df4.fillna({1:0.5,2:-1})) #将缺失值按照指定形式填充
运行结果：
0 1 2 0 -0.737618 -0.530302 -2.716457 1 0.810339 0.063028 -0.341343 2 0.070564 0.347308 -0.121137 3 -0.501875 -1.573071 -0.816077 4 -2.159196 -0.659185 -0.885185 5 0.175086 -0.954109 -0.758657 6 0.395744 -0.875943 0.950323 0 1 2 0 -0.737618 NaN NaN 1 0.810339 NaN NaN 2 0.070564 NaN NaN 3 -0.501875 NaN -0.816077 4 -2.159196 NaN -0.885185 5 0.175086 -0.954109 -0.758657 6 0.395744 -0.875943 0.950323 0 1 2 0 -0.737618 0.000000 0.000000 1 0.810339 0.000000 0.000000 2 0.070564 0.000000 0.000000 3 -0.501875 0.000000 -0.816077 4 -2.159196 0.000000 -0.885185 5 0.175086 -0.954109 -0.758657 6 0.395744 -0.875943 0.950323 0 1 2 0 -0.737618 0.500000 -1.000000 1 0.810339 0.500000 -1.000000 2 0.070564 0.500000 -1.000000 3 -0.501875 0.500000 -0.816077 4 -2.159196 0.500000 -0.885185 5 0.175086 -0.954109 -0.758657 6 0.395744 -0.875943 0.950323
2、pandas常用数学统计方法
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#pandas常用数学统计方法
arr = np.array([
[98.5,89.5,88.5],
[98.5,85.5,88],
[70,85,60],
[80,85,82]
])
df1 = DataFrame(arr,columns=["语文","数学","英语"])
print(df1)
print("=======针对列计算总统计值=======")
print(df1.describe())
print("=======默认计算各列非NaN值个数=======")
print(df1.count())
print("=======计算各行非NaN值个数=======")
print(df1.count(axis=1))
运行结果：
语文 数学 英语 0 98.5 89.5 88.5 1 98.5 85.5 88.0 2 70.0 85.0 60.0 3 80.0 85.0 82.0 =======针对列计算总统计值======= 语文 数学 英语 count 4.000000 4.000000 4.000000 mean 86.750000 86.250000 79.625000 std 14.168627 2.179449 13.412525 min 70.000000 85.000000 60.000000 25% 77.500000 85.000000 76.500000 50% 89.250000 85.250000 85.000000 75% 98.500000 86.500000 88.125000 max 98.500000 89.500000 88.500000 =======默认计算各列非NaN值个数======= 语文 4 数学 4 英语 4 dtype: int64 =======计算各行非NaN值个数======= 0 3 1 3 2 3 3 3 dtype: int64
import numpy as np
import pandas as pd
from pandas import Series,DataFrame、
#2.pandas相关系数与协方差
df2 = DataFrame({
"GDP":[12,23,34,45,56],
"air_temperature":[23,25,26,27,30],
"year":["2001","2002","2003","2004","2005"]
})
print(df2)
print("=========相关系数========")
print(df2.corr())
print("=========协方差========")
print(df2.cov())
print("=========两个量之间的相关系数========")
print(df2["GDP"].corr(df2["air_temperature"]))
print("=========两个量之间协方差========")
print(df2["GDP"].cov(df2["air_temperature"]))
运行结果：
GDP air_temperature year 0 12 23 2001 1 23 25 2002 2 34 26 2003 3 45 27 2004 4 56 30 2005 =========相关系数======== GDP air_temperature GDP 1.000000 0.977356 air_temperature 0.977356 1.000000 =========协方差======== GDP air_temperature GDP 302.5 44.0 air_temperature 44.0 6.7 =========两个量之间的相关系数======== 0.97735555485 =========两个量之间协方差======== 44.0
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#3.pandas唯一值、值计数及成员资格
df3 = DataFrame({
"order_id":["1001","1002","1003","1004","1005"],
"member_id":["m01","m01","m02","m01","m02",],
"order_amt":[345,312.2,123,250.2,235]
})
print(df3)
print("=========去重后的数组=========")
print(df3["member_id"].unique())
print("=========值出现的频率=========")
print(df3["member_id"].value_counts())
print("=========成员资格=========")
df3 = df3["member_id"]
mask = df3.isin(["m01"])
print(mask)
print(df3[mask])
运行结果：
member_id order_amt order_id 0 m01 345.0 1001 1 m01 312.2 1002 2 m02 123.0 1003 3 m01 250.2 1004 4 m02 235.0 1005 =========去重后的数组========= ['m01' 'm02'] =========值出现的频率========= m01 3 m02 2 Name: member_id, dtype: int64 =========成员资格========= 0 True 1 True 2 False 3 True 4 False Name: member_id, dtype: bool 0 m01 1 m01 3 m01 Name: member_id, dtype: object
3、pandas层次索引
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#3.pandas层次索引
data = Series([998.4,6455,5432,9765,5432],
index=[["2001","2001","2001","2002","2002"],
["苹果","香蕉","西瓜","苹果","西瓜"]]
)
print(data)
df4 = DataFrame({
"year":[2001,2001,2002,2002,2003],
"fruit":["apple","banana","apple","banana","apple"],
"production":[2345,5632,3245,6432,4532],
"profits":[245.6,432.7,534.1,354,467.8]
})
print(df4)
print("=======层次化索引=======")
df4 = df4.set_index(["year","fruit"])
print(df4)
print("=======依照索引取值=======")
print(df4.ix[2002,"apple"])
print("=======依照层次化索引统计数据=======")
print(df4.sum(level="year"))
print(df4.mean(level="fruit"))
print(df4.min(level=["year","fruit"]))
运行结果：
Python3.5 Pandas模块 缺失值处理 层次索引2001 苹果 998.4 香蕉 6455.0 西瓜 5432.0 2002 苹果 9765.0 西瓜 5432.0 dtype: float64 fruit production profits year 0 apple 2345 245.6 2001 1 banana 5632 432.7 2001 2 apple 3245 534.1 2002 3 banana 6432 354.0 2002 4 apple 4532 467.8 2003 =======层次化索引======= production profits year fruit 2001 apple 2345 245.6 banana 5632 432.7 2002 apple 3245 534.1 banana 6432 354.0 2003 apple 4532 467.8 =======依照索引取值======= production 3245.0 profits 534.1 Name: (2002, apple), dtype: float64 =======依照层次化索引统计数据======= production profits year 2001 7977 678.3 2002 9677 888.1 2003 4532 467.8 production profits fruit apple 3374 415.833333 banana 6032 393.350000 production profits year fruit 2001 apple 2345 245.6 banana 5632 432.7 2002 apple 3245 534.1 banana 6432 354.0 2003 apple 4532 467.8