Python3.5 Pandas模塊缺失值處理和層次索引實(shí)例詳解
本文實(shí)例講述了Python3.5 Pandas模塊缺失值處理和層次索引。分享給大家供大家參考，具體如下：
1、pandas缺失值處理
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("=======判斷NaN值=======")
print(df3.isnull())
print("=======判斷非NaN值=======")
print(df3.notnull())
print("=======刪除包含NaN值的行=======")
print(df3.dropna())
print("=======刪除全部為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("=======刪除包含NaN值的列=======")
print(df3.dropna(axis=1))
運(yùn)行結(jié)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
=======判斷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
=======判斷非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
=======刪除包含NaN值的行=======
name age salary gender
4 Joe 18.0 385.12 F
=======刪除全部為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
=======刪除包含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列的數(shù)據(jù)
df4.ix[:2,2] = np.nan
print(df4)
print(df4.fillna(0)) #將缺失值用傳入的指定值0替換
print(df4.fillna({1:0.5,2:-1})) #將缺失值按照指定形式填充
運(yùn)行結(jié)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常用數(shù)學(xué)統(tǒng)計(jì)方法
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#pandas常用數(shù)學(xué)統(tǒng)計(jì)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=["語(yǔ)文","數(shù)學(xué)","英語(yǔ)"])
print(df1)
print("=======針對(duì)列計(jì)算總統(tǒng)計(jì)值=======")
print(df1.describe())
print("=======默認(rèn)計(jì)算各列非NaN值個(gè)數(shù)=======")
print(df1.count())
print("=======計(jì)算各行非NaN值個(gè)數(shù)=======")
print(df1.count(axis=1))
運(yùn)行結(jié)果：
語(yǔ)文 數(shù)學(xué) 英語(yǔ)
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
=======針對(duì)列計(jì)算總統(tǒng)計(jì)值=======
語(yǔ)文 數(shù)學(xué) 英語(yǔ)
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
=======默認(rèn)計(jì)算各列非NaN值個(gè)數(shù)=======
語(yǔ)文 4
數(shù)學(xué) 4
英語(yǔ) 4
dtype: int64
=======計(jì)算各行非NaN值個(gè)數(shù)=======
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相關(guān)系數(shù)與協(xié)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("=========相關(guān)系數(shù)========")
print(df2.corr())
print("=========協(xié)方差========")
print(df2.cov())
print("=========兩個(gè)量之間的相關(guān)系數(shù)========")
print(df2["GDP"].corr(df2["air_temperature"]))
print("=========兩個(gè)量之間協(xié)方差========")
print(df2["GDP"].cov(df2["air_temperature"]))
運(yùn)行結(jié)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
=========相關(guān)系數(shù)========
GDP air_temperature
GDP 1.000000 0.977356
air_temperature 0.977356 1.000000
=========協(xié)方差========
GDP air_temperature
GDP 302.5 44.0
air_temperature 44.0 6.7
=========兩個(gè)量之間的相關(guān)系數(shù)========
0.97735555485
=========兩個(gè)量之間協(xié)方差========
44.0
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#3.pandas唯一值、值計(jì)數(shù)及成員資格
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("=========去重后的數(shù)組=========")
print(df3["member_id"].unique())
print("=========值出現(xiàn)的頻率=========")
print(df3["member_id"].value_counts())
print("=========成員資格=========")
df3 = df3["member_id"]
mask = df3.isin(["m01"])
print(mask)
print(df3[mask])
運(yùn)行結(jié)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
=========去重后的數(shù)組=========
['m01' 'm02']
=========值出現(xiàn)的頻率=========
m01 3
m02 2
Name: member_id, dtype: int64
=========成員資格=========
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層次索引
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
#3.pandas層次索引
data = Series([998.4,6455,5432,9765,5432],
index=[["2001","2001","2001","2002","2002"],
["蘋果","香蕉","西瓜","蘋果","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("=======層次化索引=======")
df4 = df4.set_index(["year","fruit"])
print(df4)
print("=======依照索引取值=======")
print(df4.ix[2002,"apple"])
print("=======依照層次化索引統(tǒng)計(jì)數(shù)據(jù)=======")
print(df4.sum(level="year"))
print(df4.mean(level="fruit"))
print(df4.min(level=["year","fruit"]))
運(yùn)行結(jié)果：
2001 蘋果 998.4
香蕉 6455.0
西瓜 5432.0
2002 蘋果 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
=======層次化索引=======
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
=======依照層次化索引統(tǒng)計(jì)數(shù)據(jù)=======
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
更多關(guān)于Python相關(guān)內(nèi)容感興趣的讀者可查看本站專題：《Python數(shù)學(xué)運(yùn)算技巧總結(jié)》、《Python數(shù)據(jù)結(jié)構(gòu)與算法教程》、《Python函數(shù)使用技巧總結(jié)》、《Python字符串操作技巧匯總》、《Python入門與進(jìn)階經(jīng)典教程》及《Python文件與目錄操作技巧匯總》
希望本文所述對(duì)大家Python程序設(shè)計(jì)有所幫助。
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