pandas6:DataFrame非值数据(Nan)的处理

红太狼 2023-02-26 12:24 38阅读 0赞

**Pandas中有哪些非值数据**

# 1. NaN 是什么 #

NaN是被遗失的，不属于任何类型

from numpy import NaN,nan
    print(nan)

nan

print(NaN==True)
    print(NaN==False)
    print(NaN==0)
    print(NaN=='')
    print(NaN==NaN)
    print(NaN==nan)

False
    False
    False
    False
    False
    False

import pandas as pd
    x = NaN
    y = nan
    n = 20
    print(pd.isnull(x))
    print(pd.isnull(y))
    print(pd.notnull(n))

True
    True
    True

# 2.数据遗失的原因 #

## 2.1数据缺失造成的NaN ##

import pandas as pd
    # 装载数据
    
    visited_file = './data/survey_visited.csv'
    print(pd.read_csv(visited_file))

# 不将控制设置为NaN
    print(pd.read_csv(visited_file,na_values=[' '],keep_default_na=False))

ident   site       dated
    0    619   DR-1  1927-02-08
    1    622   DR-1  1927-02-10
    2    734   DR-3  1939-01-07
    3    735   DR-3  1930-01-12
    4    751   DR-3  1930-02-26
    5    752   DR-3            
    6    837  MSK-4  1932-01-14
    7    844   DR-1  1932-03-22

## 2.2合并数据导致的NaN ##

# 合并数据
    visited = pd.read_csv('./data/survey_visited.csv')
    survey = pd.read_csv('./data/survey_survey.csv')
    print(visited)
    print(survey)

ident   site       dated
    0    619   DR-1  1927-02-08
    1    622   DR-1  1927-02-10
    2    734   DR-3  1939-01-07
    3    735   DR-3  1930-01-12
    4    751   DR-3  1930-02-26
    5    752   DR-3         NaN
    6    837  MSK-4  1932-01-14
    7    844   DR-1  1932-03-22
        taken person quant  reading
    0     619   dyer   rad     9.82
    1     619   dyer   sal     0.13
    2     622   dyer   rad     7.80
    3     622   dyer   sal     0.09
    4     734     pb   rad     8.41
    5     734   lake   sal     0.05
    6     734     pb  temp   -21.50
    7     735     pb   rad     7.22
    8     735    NaN   sal     0.06
    9     735    NaN  temp   -26.00
    10    751     pb   rad     4.35
    11    751     pb  temp   -18.50
    12    751   lake   sal     0.10
    13    752   lake   rad     2.19
    14    752   lake   sal     0.09
    15    752   lake  temp   -16.00
    16    752    roe   sal    41.60
    17    837   lake   rad     1.46
    18    837   lake   sal     0.21
    19    837    roe   sal    22.50
    20    844    roe   rad    11.25

vs = visited.merge(survey,left_on='ident',right_on='taken')
    print(vs)

ident   site       dated  taken person quant  reading
    0     619   DR-1  1927-02-08    619   dyer   rad     9.82
    1     619   DR-1  1927-02-08    619   dyer   sal     0.13
    2     622   DR-1  1927-02-10    622   dyer   rad     7.80
    3     622   DR-1  1927-02-10    622   dyer   sal     0.09
    4     734   DR-3  1939-01-07    734     pb   rad     8.41
    5     734   DR-3  1939-01-07    734   lake   sal     0.05
    6     734   DR-3  1939-01-07    734     pb  temp   -21.50
    7     735   DR-3  1930-01-12    735     pb   rad     7.22
    8     735   DR-3  1930-01-12    735    NaN   sal     0.06
    9     735   DR-3  1930-01-12    735    NaN  temp   -26.00
    10    751   DR-3  1930-02-26    751     pb   rad     4.35
    11    751   DR-3  1930-02-26    751     pb  temp   -18.50
    12    751   DR-3  1930-02-26    751   lake   sal     0.10
    13    752   DR-3         NaN    752   lake   rad     2.19
    14    752   DR-3         NaN    752   lake   sal     0.09
    15    752   DR-3         NaN    752   lake  temp   -16.00
    16    752   DR-3         NaN    752    roe   sal    41.60
    17    837  MSK-4  1932-01-14    837   lake   rad     1.46
    18    837  MSK-4  1932-01-14    837   lake   sal     0.21
    19    837  MSK-4  1932-01-14    837    roe   sal    22.50
    20    844   DR-1  1932-03-22    844    roe   rad    11.25

## 2.3用户输入到时的NaN ##

scientisits = pd.DataFrame({ 
        'Name':['Bill','Mike'],
        'Occupation':['Chemist','Statist'],
    })
    print(scientisits)

Name Occupation
    0  Bill    Chemist
    1  Mike    Statist

from numpy import nan
    
    scientisits['missing'] = nan
    print(scientisits)

Name Occupation  missing
    0  Bill    Chemist      NaN
    1  Mike    Statist      NaN

## 2.4重建索引 ##

gapminder = pd.read_csv('./data/gapminder.tsv',sep='\t')
    print(gapminder.head(5))

country continent  year  lifeExp       pop   gdpPercap
    0  Afghanistan      Asia  1952   28.801   8425333  779.445314
    1  Afghanistan      Asia  1957   30.332   9240934  820.853030
    2  Afghanistan      Asia  1962   31.997  10267083  853.100710
    3  Afghanistan      Asia  1967   34.020  11537966  836.197138
    4  Afghanistan      Asia  1972   36.088  13079460  739.981106

life_exp = gapminder.groupby(['year'])['lifeExp'].mean()

print(life_exp)

year
    1952    49.057620
    1957    51.507401
    1962    53.609249
    1967    55.678290
    1972    57.647386
    1977    59.570157
    1982    61.533197
    1987    63.212613
    1992    64.160338
    1997    65.014676
    2002    65.694923
    2007    67.007423
    Name: lifeExp, dtype: float64

### 缺失部分数据为NaN ###

print(life_exp.loc[range(2000,2003)])

year
    2000          NaN
    2001          NaN
    2002    65.694923
    Name: lifeExp, dtype: float64

## 3.处理非值数据 ##

### 3.1填充NaN ###

import pandas as pd

gapminder = pd.read_csv('./data/gapminder.tsv',sep='\t')
    print(gapminder.head(5))

life_exp = gapminder.groupby(['year'])['lifeExp'].mean()
    a = life_exp.loc[range(2000,2010)]
    print(a)

year
    2000          NaN
    2001          NaN
    2002    65.694923
    2003          NaN
    2004          NaN
    2005          NaN
    2006          NaN
    2007    67.007423
    2008          NaN
    2009          NaN
    Name: lifeExp, dtype: float64

#### 填充指定值 ####

print(a.fillna(0))
    print(a.fillna('*'))

year
    2000     0.000000
    2001     0.000000
    2002    65.694923
    2003     0.000000
    2004     0.000000
    2005     0.000000
    2006     0.000000
    2007    67.007423
    2008     0.000000
    2009     0.000000
    Name: lifeExp, dtype: float64
    year
    2000          *
    2001          *
    2002    65.6949
    2003          *
    2004          *
    2005          *
    2006          *
    2007    67.0074
    2008          *
    2009          *
    Name: lifeExp, dtype: object

#### forward 填充 ####

print(a.fillna(method='ffill'))

year
    2000          NaN
    2001          NaN
    2002    65.694923
    2003    65.694923
    2004    65.694923
    2005    65.694923
    2006    65.694923
    2007    67.007423
    2008    67.007423
    2009    67.007423
    Name: lifeExp, dtype: float64

#### backward 填充 ####

print(a.fillna(method='bfill'))

year
    2000    65.694923
    2001    65.694923
    2002    65.694923
    2003    67.007423
    2004    67.007423
    2005    67.007423
    2006    67.007423
    2007    67.007423
    2008          NaN
    2009          NaN
    Name: lifeExp, dtype: float64

#### 先bfill后ffill填充 ####

print(a.fillna(method='bfill').fillna(method='ffill'))

year
    2000    65.694923
    2001    65.694923
    2002    65.694923
    2003    67.007423
    2004    67.007423
    2005    67.007423
    2006    67.007423
    2007    67.007423
    2008    67.007423
    2009    67.007423
    Name: lifeExp, dtype: float64

#### 线性插值 ####

print(a)
    print(a.interpolate())

year
    2000          NaN
    2001          NaN
    2002    65.694923
    2003          NaN
    2004          NaN
    2005          NaN
    2006          NaN
    2007    67.007423
    2008          NaN
    2009          NaN
    Name: lifeExp, dtype: float64
    year
    2000          NaN
    2001          NaN
    2002    65.694923
    2003    65.957423
    2004    66.219923
    2005    66.482423
    2006    66.744923
    2007    67.007423
    2008    67.007423
    2009    67.007423
    Name: lifeExp, dtype: float64

aa = pd.Series([NaN,NaN,2,NaN,4,NaN,6,NaN,8,NaN,NaN,NaN])

print(aa.interpolate())

0     NaN
    1     NaN
    2     2.0
    3     3.0
    4     4.0
    5     5.0
    6     6.0
    7     7.0
    8     8.0
    9     8.0
    10    8.0
    11    8.0
    dtype: float64

### 3.2删除包含NaN的行 ###

print(a)
    print(a.dropna())

year
    2000          NaN
    2001          NaN
    2002    65.694923
    2003          NaN
    2004          NaN
    2005          NaN
    2006          NaN
    2007    67.007423
    2008          NaN
    2009          NaN
    Name: lifeExp, dtype: float64
    year
    2002    65.694923
    2007    67.007423
    Name: lifeExp, dtype: float64