pandas3: Pandas基本操作

r囧r小猫 2023-02-26 12:22 12阅读 0赞

import pandas as pd
    persons = pd.DataFrame({ 
        'Name':['Rosaline Franklin','William Gosset'],
        'Occupation':['Chemist','Statistician'],
        'Born':['1920-07-25','1876-06-13'],
        'Died':['1958-04-16','1937-10-16'],
        'Age':[37,61]},columns=['Occupation','Born','Died','Age'],index=['Rosaline Franklin','William Gosset'])
    print(persons)

Occupation        Born        Died  Age
    Rosaline Franklin       Chemist  1920-07-25  1958-04-16   37
    William Gosset     Statistician  1876-06-13  1937-10-16   61

first_row = persons.loc['Rosaline Franklin']

# 1.Serial基本操作 #

## 1.1Serial属性 ##

print(type(first_row))
    print(first_row)
    print(first_row.index)
    print(first_row.keys())
    print(first_row.values)
    print(first_row.index[0])

<class 'pandas.core.series.Series'>
    Occupation       Chemist
    Born          1920-07-25
    Died          1958-04-16
    Age                   37
    Name: Rosaline Franklin, dtype: object
    Index(['Occupation', 'Born', 'Died', 'Age'], dtype='object')
    Index(['Occupation', 'Born', 'Died', 'Age'], dtype='object')
    ['Chemist' '1920-07-25' '1958-04-16' 37]
    Occupation

## 1.2Serial方法 ##

import pandas as pd
    import matplotlib as plt
    persons = pd.DataFrame({ 
        'Name':['Rosaline Franklin','William Gosset'],
        'Occupation':['Chemist','Statistician'],
        'Born':['1920-07-25','1876-06-13'],
        'Died':['1958-04-16','1937-10-16'],
        'Age':[37,61]},columns=['Occupation','Born','Died','Age'],index=['Rosaline Franklin','William Gosset'])
    print(persons)

Occupation        Born        Died  Age
    Rosaline Franklin       Chemist  1920-07-25  1958-04-16   37
    William Gosset     Statistician  1876-06-13  1937-10-16   61

ages = persons['Age']

print(type(ages))
    print(ages)
    print(ages.mean())
    print(ages.std())
    print(ages.min())
    print(ages.max())
    print(ages.sort_values(ascending=False))
    print("***"*8)
    print(ages.append(ages))

<class 'pandas.core.series.Series'>
    Rosaline Franklin    37
    William Gosset       61
    Name: Age, dtype: int64
    49.0
    16.97056274847714
    37
    61
    William Gosset       61
    Rosaline Franklin    37
    Name: Age, dtype: int64
    ************************
    Rosaline Franklin    37
    William Gosset       61
    Rosaline Franklin    37
    William Gosset       61
    Name: Age, dtype: int64

ages.hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7f742b86f290>

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## 1.3Serial 过滤条件 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')

ages = scientists['Age']
    print(ages)

0    37
    1    61
    2    90
    3    66
    4    56
    5    45
    6    41
    7    77
    Name: Age, dtype: int64

print(ages.describe())

count     8.000000
    mean     59.125000
    std      18.325918
    min      37.000000
    25%      44.000000
    50%      58.500000
    75%      68.750000
    max      90.000000
    Name: Age, dtype: float64

print(ages[ages > ages.mean()])
    print(ages > ages.mean())
    print(type(ages > ages.mean()))

1    61
    2    90
    3    66
    7    77
    Name: Age, dtype: int64
    0    False
    1     True
    2     True
    3     True
    4    False
    5    False
    6    False
    7     True
    Name: Age, dtype: bool
    <class 'pandas.core.series.Series'>

print(ages[[True,True,True,False,False,True,True,True]])

0    37
    1    61
    2    90
    5    45
    6    41
    7    77
    Name: Age, dtype: int64

## 1.4Serial 向量操作 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')
    ages = scientists['Age']
    print(ages)

0    37
    1    61
    2    90
    3    66
    4    56
    5    45
    6    41
    7    77
    Name: Age, dtype: int64

print(ages+ages)

0     74
    1    122
    2    180
    3    132
    4    112
    5     90
    6     82
    7    154
    Name: Age, dtype: int64

print(ages*ages)

0    1369
    1    3721
    2    8100
    3    4356
    4    3136
    5    2025
    6    1681
    7    5929
    Name: Age, dtype: int64

print(ages+100)

0    137
    1    161
    2    190
    3    166
    4    156
    5    145
    6    141
    7    177
    Name: Age, dtype: int64

print(ages+pd.Series([1,100]))

0     38.0
    1    161.0
    2      NaN
    3      NaN
    4      NaN
    5      NaN
    6      NaN
    7      NaN
    dtype: float64

import numpy as np
    #print(ages + np.array([1,2,3])) # error

# 2.DataFrame基本操作 #

## 2.1DataFrame的条件过滤 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')

print(scientists)

Name        Born        Died  Age          Occupation
    0     Rosaline Franklin  1920-07-25  1958-04-16   37             Chemist
    1        William Gosset  1876-06-13  1937-10-16   61        Statistician
    2  Florence Nightingale  1820-05-12  1910-08-13   90               Nurse
    3           Marie Curie  1867-11-07  1934-07-04   66             Chemist
    4         Rachel Carson  1907-05-27  1964-04-14   56           Biologist
    5             John Snow  1813-03-15  1858-06-16   45           Physician
    6           Alan Turing  1912-06-23  1954-06-07   41  Computer Scientist
    7          Johann Gauss  1777-04-30  1855-02-23   77       Mathematician

print(scientists[scientists['Age'] > scientists['Age'].mean()])

Name        Born        Died  Age     Occupation
    1        William Gosset  1876-06-13  1937-10-16   61   Statistician
    2  Florence Nightingale  1820-05-12  1910-08-13   90          Nurse
    3           Marie Curie  1867-11-07  1934-07-04   66        Chemist
    7          Johann Gauss  1777-04-30  1855-02-23   77  Mathematician

print(scientists.loc[[True,True,0,1]])

Name        Born        Died  Age    Occupation
    1     William Gosset  1876-06-13  1937-10-16   61  Statistician
    1     William Gosset  1876-06-13  1937-10-16   61  Statistician
    0  Rosaline Franklin  1920-07-25  1958-04-16   37       Chemist
    1     William Gosset  1876-06-13  1937-10-16   61  Statistician

print(scientists.iloc[[1,3,4]])

Name        Born        Died  Age    Occupation
    1  William Gosset  1876-06-13  1937-10-16   61  Statistician
    3     Marie Curie  1867-11-07  1934-07-04   66       Chemist
    4   Rachel Carson  1907-05-27  1964-04-14   56     Biologist

print(scientists[['Age','Name']][scientists['Age'] > scientists['Age'].mean()])

Age                  Name
    1   61        William Gosset
    2   90  Florence Nightingale
    3   66           Marie Curie
    7   77          Johann Gauss

print(scientists[['Age','Name']][scientists['Age'] > scientists['Age'].mean()].iloc[[1,2]])

Age                  Name
    2   90  Florence Nightingale
    3   66           Marie Curie

## 2.2DataFrame的向量操作 ##

import pandas as pd
    d1 = pd.DataFrame({ 
        'a':[1,2],
        'b':[4,5]
    })
    d2 = pd.DataFrame({ 
        'a':[4,5],
        'b':[1,2]
    })
    print(d1)
    print(d2)

a  b
    0  1  4
    1  2  5
       a  b
    0  4  1
    1  5  2

print(d1+d2)

a  b
    0  5  5
    1  7  7

print(d1*d2)

a   b
    0   4   4
    1  10  10

print(d1**d2)

a   b
    0   1   4
    1  32  25

## 2.3向DataFrame添加列 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')
    print(scientists)

print(scientists['Born'].dtype)
    print(scientists['Died'].dtype)

object
    object

born_datetime = pd.to_datetime(scientists['Born'],format='%Y-%m-%d')
    died_datetime = pd.to_datetime(scientists['Died'],format='%Y-%m-%d')
    print(type(born_datetime))

scientists['born_dt'],scientists['died_dt'] = (born_datetime,died_datetime)

print(scientists['born_dt'].dtype)
    scientists.head()

datetime64[ns]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>Name</th> 
   <th>Born</th> 
   <th>Died</th> 
   <th>Age</th> 
   <th>Occupation</th> 
   <th>born_dt</th> 
   <th>died_dt</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>Rosaline Franklin</td> 
   <td>1920-07-25</td> 
   <td>1958-04-16</td> 
   <td>37</td> 
   <td>Chemist</td> 
   <td>1920-07-25</td> 
   <td>1958-04-16</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>William Gosset</td> 
   <td>1876-06-13</td> 
   <td>1937-10-16</td> 
   <td>61</td> 
   <td>Statistician</td> 
   <td>1876-06-13</td> 
   <td>1937-10-16</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>Florence Nightingale</td> 
   <td>1820-05-12</td> 
   <td>1910-08-13</td> 
   <td>90</td> 
   <td>Nurse</td> 
   <td>1820-05-12</td> 
   <td>1910-08-13</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>Marie Curie</td> 
   <td>1867-11-07</td> 
   <td>1934-07-04</td> 
   <td>66</td> 
   <td>Chemist</td> 
   <td>1867-11-07</td> 
   <td>1934-07-04</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>Rachel Carson</td> 
   <td>1907-05-27</td> 
   <td>1964-04-14</td> 
   <td>56</td> 
   <td>Biologist</td> 
   <td>1907-05-27</td> 
   <td>1964-04-14</td> 
  </tr> 
 </tbody> 
</table>

## 2.4直接修改DataFrame的值 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')
    print(scientists)

import random
    ageList = [random.randint(30,100) for i in range(8)]

scientists['Age'] = ageList

print(scientists)

Name        Born        Died  Age          Occupation
    0     Rosaline Franklin  1920-07-25  1958-04-16   75             Chemist
    1        William Gosset  1876-06-13  1937-10-16   99        Statistician
    2  Florence Nightingale  1820-05-12  1910-08-13   86               Nurse
    3           Marie Curie  1867-11-07  1934-07-04   67             Chemist
    4         Rachel Carson  1907-05-27  1964-04-14   71           Biologist
    5             John Snow  1813-03-15  1858-06-16   35           Physician
    6           Alan Turing  1912-06-23  1954-06-07   78  Computer Scientist
    7          Johann Gauss  1777-04-30  1855-02-23   61       Mathematician

## 2.5删除DataFrame的列 ##

import pandas as pd
    scientists = pd.read_csv('./data/scientists.csv')
    print(scientists.columns)

Index(['Name', 'Born', 'Died', 'Age', 'Occupation'], dtype='object')

scientists_drop = scientists.drop(['Age','Died'],axis=1)

print(scientists)

print(scientists_drop)

Name        Born          Occupation
    0     Rosaline Franklin  1920-07-25             Chemist
    1        William Gosset  1876-06-13        Statistician
    2  Florence Nightingale  1820-05-12               Nurse
    3           Marie Curie  1867-11-07             Chemist
    4         Rachel Carson  1907-05-27           Biologist
    5             John Snow  1813-03-15           Physician
    6           Alan Turing  1912-06-23  Computer Scientist
    7          Johann Gauss  1777-04-30       Mathematician