python如何让dataframe数据展示在终端所有列 python dataframe series

本課主題

• Numpy 的介绍和操作实战
• Series 的介绍和操作实战
• DataFrame 的介绍和操作实战

 

Numpy 的介绍和操作实战

numpy 是 Python 在数据计算领域里很常用的模块

import numpy as np
np.array([11,22,33]) #接受一个列表数据

1. 创建 numpy array
   

>>> import numpy as np
>>> mylist = [1,2,3]
>>> x = np.array(mylist)
>>> x
array([1, 2, 3])
>>> y = np.array([4,5,6])
>>> y
array([4, 5, 6])
>>> m = np.array([[7,8,9],[10,11,12]])
>>> m
array([[ 7,  8,  9],
       [10, 11, 12]])

1. 创建 numpy array(例子)
2. 查看 numpy array 的
   

>>> m.shape #array([1, 2, 3])
(2, 3)

>>> x.shape #array([4, 5, 6])
(3,)

>>> y.shape #array([[ 7,  8,  9], [10, 11, 12]])
(3,)

1. View Code
2. numpy.arrange
   

>>> n = np.arange(0,30,2)
>>> n
array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])

1. numpy.arrange( )(例子)
2. 改变numpy array的位置
   

>>> n = np.arange(0,30,2)
>>> n
array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])
>>> n.shape
(15,)

>>> n = n.reshape(3,5) #从15列改成3列5行
>>> n

array([[ 0,  2,  4,  6,  8],
       [10, 12, 14, 16, 18],
       [20, 22, 24, 26, 28]])

1. numpy.reshape( )(例子一)

>>> o = np.linspace(0,4,9)
>>> o
array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ])
>>> o.resize(3,3)
>>> o
array([[ 0. ,  0.5,  1. ],
       [ 1.5,  2. ,  2.5],
       [ 3. ,  3.5,  4. ]])

1. numpy.reshape( )(例子二)
2. numpy.ones( ) ，numpy.zeros( )，numpy.eye( )
   

>>> r1 = np.ones((3,2))
>>> r1
array([[ 1.,  1.],
       [ 1.,  1.],
       [ 1.,  1.]])

>>> r1 = np.zeros((2,3))
>>> r1
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])

>>> r2 = np.eye(3)
>>> r2
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

1. numpy.ones/zeros/eye( )(例子)

可以定义整数

>>> r5 = np.ones([2,3], int)
>>> r5
array([[1, 1, 1],
       [1, 1, 1]])

>>> r5 = np.ones([2,3])
>>> r5
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])

1. numpy.ones(x,int)(例子)
2. numpy.diag( )
   

>>> y = np.array([4,5,6])
>>> y
array([4, 5, 6])

>>> np.diag(y)
array([[4, 0, 0],
       [0, 5, 0],
       [0, 0, 6]])

1. diag( )(例子)
2. 复制 numpy array
   

>>> r3 = np.array([1,2,3] * 3)
>>> r3
array([1, 2, 3, 1, 2, 3, 1, 2, 3])

>>> r4 = np.repeat([1,2,3],3)
>>> r4
array([1, 1, 1, 2, 2, 2, 3, 3, 3])

1. 复制numpy array(例子)
2. numpy中的 vstack和 hstack
   

>>> r5 = np.ones([2,3], int)
>>> r5
array([[1, 1, 1],
       [1, 1, 1]])

>>> r6 = np.vstack([r5,2*r5])
>>> r6
array([[1, 1, 1],
       [1, 1, 1],
       [2, 2, 2],
       [2, 2, 2]])

>>> r7 = np.hstack([r5,2*r5])
>>> r7
array([[1, 1, 1, 2, 2, 2],
       [1, 1, 1, 2, 2, 2]])

1. numpy.vstack( )和np.hstack( )(例子)
2. numpy 中的加减乘除操作一 (＋－＊／）
   

>>> mylist = [1,2,3]
>>> x = np.array(mylist)
>>> y = np.array([4,5,6])

>>> x+y
array([5, 7, 9])

>>> x-y
array([-3, -3, -3])

>>> x*y
array([ 4, 10, 18])

>>> x**2
array([1, 4, 9])

>>> x.dot(y)
32

1. numpy中的加减乘除(例子一)
2. numpy 中的加减乘除操作二：sum( )、max( )、min( )、mean( )、std( )
   

>>> a = np.array([1,2,3,4,5])
>>> a.sum()
15

>>> a.max()
5

>>> a.min()
1

>>> a.mean()
3.0

>>> a.std()
1.4142135623730951

>>> a.argmax()
4

>>> a.argmin()
0

1. numpy中的加减乘除(例子二)
2. 查看numpy array 的数据类型
   

>>> y = np.array([4,5,6])
>>> z = np.array([y, y**2])
>>> z
array([[ 4,  5,  6],
       [16, 25, 36]])

>>> z.shape
(2, 3)

>>> z.T.shape
(3, 2)

>>> z.dtype
dtype('int64')

>>> z = z.astype('f')

>>> z.dtype
dtype('float32')

1. numpy array 的数据类型
2. numpy 中的索引和切片
   

>>> s = np.arange(13)
>>> s
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

>>> s = np.arange(13) ** 2
>>> s
array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144])

>>> s[0],s[4],s[0:3]
(0, 16, array([0, 1, 4]))

>>> s[1:5]
array([ 1,  4,  9, 16])

>>> s[-4:]
array([ 81, 100, 121, 144])

>>> s[-5:-2]
array([ 64,  81, 100])

1. numpy索引和切片(例子一)

>>> r = np.arange(36)
>>> r.resize((6,6))
>>> r
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])

>>> r[2,2]
14

>>> r[3,3:6]
array([21, 22, 23])

>>> r[:2,:-1]
array([[ 0,  1,  2,  3,  4],
       [ 6,  7,  8,  9, 10]])

>>> r[-1,::2]
array([30, 32, 34])

>>> r[r > 30] #取r大于30的数据
array([31, 32, 33, 34, 35])

>>> re2 = r[r > 30] = 30
>>> re2
30
>>> r8 = r[:3,:3]
>>> r8

array([[ 0,  1,  2],
       [ 6,  7,  8],
       [12, 13, 14]])

>>> r8[:] = 0

>>> r8
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])

>>> r 
array([[ 0,  0,  0,  3,  4,  5],
       [ 0,  0,  0,  9, 10, 11],
       [ 0,  0,  0, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 30, 30, 30, 30, 30]])

1. numpy索引和切片(例子二)
2. copy numpy array 的数组
   

>>> r = np.arange(36)
>>> r.resize((6,6))
>>> r_copy = r.copy()
>>> r
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])

>>> r_copy
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])

>>> r_copy[:] = 10

>>> r_copy
array([[10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 10],
       [10, 10, 10, 10, 10, 10]])

1. copy( )例子
2. 其他操作
   

>>> test = np.random.randint(0,10,(4,3))
>>> test
array([[3, 5, 2],
       [7, 7, 9],
       [8, 9, 2],
       [2, 9, 1]])

>>> for row in test:
...     print(row)
... 
[3 5 2]
[7 7 9]
[8 9 2]
[2 9 1]

>>> for i in range(len(test)):
...     print(test[i])
... 
[3 5 2]
[7 7 9]
[8 9 2]
[2 9 1]

>>> for i, row in enumerate(test):
...     print('row', i, 'is', row)
... 
row 0 is [3 5 2]
row 1 is [7 7 9]
row 2 is [8 9 2]
row 3 is [2 9 1]

>>> test2 = test ** 2
>>> test2
array([[ 9, 25,  4],
       [49, 49, 81],
       [64, 81,  4],
       [ 4, 81,  1]])

>>> for i,j, in zip(test,test2):
...     print(i, '+', j, '=', i + j)
... 
[3 5 2] + [ 9 25  4] = [12 30  6]
[7 7 9] + [49 49 81] = [56 56 90]
[8 9 2] + [64 81  4] = [72 90  6]
[2 9 1] + [ 4 81  1] = [ 6 90  2]
>>>

1. numpy array 的其他操作例子

 

Series 的介绍和操作实战

如果是输入一个字典类型的话，字典的键会自动变成 Index，然后它的值是Value

from pandas import Series, DataFrame
import pandas as pd
pd.Series(['Dog','Bear','Tiger','Moose','Giraffe','Hippopotamus','Mouse'], name='Animals') #接受一个列表类型的数据

def __init__(self, data=None, index=None, dtype=None, name=None,
                 copy=False, fastpath=False):

Series的__init__方法

1. 创建 Series 类型
   第一：你可以传入一个列表或者是字典来创建 Series，如果传入的是列表，Python会自动把 [0,1,2] 作为 Series 的索引。
   第二：如果你传入的是字符串类型的数据，Series 返回的dtype是object；如果你传入的是数字类型的数据，Series 返回的dtype是int64
   

>>> from pandas import Series, DataFrame
>>> import pandas as pd
>>> animals = ['Tiger','Bear','Moose']

>>> s1 = pd.Series(animals) 
>>> s1
0    Tiger
1     Bear
2    Moose
dtype: object

>>> s2 = pd.Series([1,2,3])
>>> s2
0    1
1    2
2    3
dtype: int64

1. 创建 Series

Series如何处理 NaN的数据？

>>> animals2 = ['Tiger','Bear',None]
>>> s3 = pd.Series(animals2) 
>>> s3
0    Tiger
1     Bear
2     None
dtype: object

>>> s4 = pd.Series([1,2,None]) 
>>> s4
0    1.0
1    2.0
2    NaN
dtype: float64

1. Series NaN数据(范例)
2. Series 中的 NaN数据和如何检查 NaN数据是否相等，这时候需要调用 np.isnan( )方法
   

>>> import numpy as np
>>> np.nan == None
False

>>> np.nan == np.nan
False

>>> np.isnan(np.nan)
True

1. np.isnan( )
2. Series 默应 Index 是 [0,1,2]，但也可以自定义 Series 中的Index
   

>>> import numpy as np
>>> sports = {
...     'Archery':'Bhutan',
...     'Golf':'Scotland',
...     'Sumo':'Japan',
...     'Taekwondo':'South Korea'
... }

>>> s5 = pd.Series(sports)
>>> s5
Archery           Bhutan
Golf            Scotland
Sumo               Japan
Taekwondo    South Korea
dtype: object

>>> s5.index
Index(['Archery', 'Golf', 'Sumo', 'Taekwondo'], dtype='object')

1. 自定义 Series 中的Index(例子一)

>>> from pandas import Series, DataFrame
>>> import pandas as pd
>>> s6 = pd.Series(['Tiger','Bear','Moose'], index=['India','America','Canada'])
>>> s6
India      Tiger
America     Bear
Canada     Moose
dtype: object

1. 自定义 Series 中的Index(例子一)
2. 查询 Series 的数据有两种方法，第一是通过index方法 e.g. s.iloc[2]；第二是通过label方法 e.g. s.loc['America']
   

>>> from pandas import Series, DataFrame
>>> import pandas as pd
>>> s6
India      Tiger
America     Bear
Canada     Moose
dtype: object

>>> s6.iloc[2] #获取 index2位置的数据
'Moose'

>>> s6.loc['America'] #获取 label: America 的值
'Bear'

>>> s6[1] #底层调用了 s6.iloc[1]
'Bear'

>>> s6['India'] #底层调用了 s6.loc['India']
'Tiger'

1. 查询Series(例子)
2. Series 的数据操作: sum( )，它底层也是调用 numpy 的方法
   

>>> s7 = pd.Series([100.00,120.00,101.00,3.00])
>>> s7
0    100.0
1    120.0
2    101.0
3      3.0
dtype: float64

>>> total = 0
>>> for item in s7:
...     total +=item
... 
>>> total
324.0

>>> total2 =  np.sum(s7)
>>> total2
324.0

1. np.sum(s7)

>>> s8 = pd.Series(np.random.randint(0,1000,10000))
>>> s8.head()
0     25
1    399
2    326
3    479
4    603
dtype: int64
>>> len(s8)
10000

1. head( )例子
2. Series 也可以存储混合型数据
   

>>> s9 = pd.Series([1,2,3])
>>> s9.loc['Animals'] = 'Bears'
>>> s9
0              1
1              2
2              3
Animals    Bears
dtype: object

1. 混合型存储数据(例子)
2. Series 中的 append( ) 用法
   

>>> original_sports = pd.Series({'Archery':'Bhutan',
...                              'Golf':'Scotland',
...                              'Sumo':'Japan',
...                              'Taekwondo':'South Korea'})
>>> cricket_loving_countries = pd.Series(['Australia', 'Barbados','Pakistan','England'],
...                                      index=['Cricket','Cricket','Cricket','Cricket'])
>>> all_countries = original_sports.append(cricket_loving_countries)

>>> original_sports
Archery           Bhutan
Golf            Scotland
Sumo               Japan
Taekwondo    South Korea
dtype: object

>>> cricket_loving_countries
Cricket    Australia
Cricket     Barbados
Cricket     Pakistan
Cricket      England
dtype: object

>>> all_countries
Archery           Bhutan
Golf            Scotland
Sumo               Japan
Taekwondo    South Korea
Cricket        Australia
Cricket         Barbados
Cricket         Pakistan
Cricket          England
dtype: object

1. Series类型的append( )

 

DataFrame

这是创建一个DataFrame对象的基本语句：接受字典类型的数据；字典中的Key (e.g. Animals, Owners) 对应 DataFrame中的Columns，它的 Value 也相当于数据库表中的每一行数据。

data = {
        'Animals':['Dog','Bear','Tiger','Moose','Giraffe','Hippopotamus','Mouse'],
        'Owners':['Chris','Kevyn','Bob','Vinod','Daniel','Fil','Stephanie']
}
df = DataFrame(data, columns=['Animals','Owners'])

 

基础操作

1. 创建DataFrame
   

>>> from pandas import Series, DataFrame
>>> import pandas as pd
>>> data = {'name':['yahoo','google','facebook'],
...         'marks':[200,400,800],
...         'price':[9,3,7]}
>>> df = DataFrame(data)
>>> df
   marks      name  price
0    200     yahoo      9
1    400    google      3
2    800  facebook      7

1. 创建DataFrame(例子一)

>>> df2 = DataFrame(data, columns=['name','price','marks'])
>>> df2
       name  price  marks
0     yahoo      9    200
1    google      3    400
2  facebook      7    800

>>> df3 = DataFrame(data, columns=['name','price','marks'], index=['a','b','c'])
>>> df3
       name  price  marks
a     yahoo      9    200
b    google      3    400
c  facebook      7    800

>>> df4 = DataFrame(data, columns=['name','price','marks', 'debt'], index=['a','b','c'])
>>> df4
       name  price  marks debt
a     yahoo      9    200  NaN
b    google      3    400  NaN
c  facebook      7    800  NaN

1. 创建DataFrame(例子二)

>>> import pandas as pd
>>> purchase_1 = pd.Series({'Name':'Chris','Item Purchased':'Dog Food','Cost':22.50})
>>> purchase_2 = pd.Series({'Name':'Kelvin','Item Purchased':'Kitty Litter','Cost':2.50})
>>> purchase_3 = pd.Series({'Name':'Vinod','Item Purchased':'Bird Seed','Cost':5.00})
>>> 
>>> df = pd.DataFrame([purchase_1,purchase_2,purchase_3],index=['Store 1','Store 2','Store 1'])
>>> df
         Cost Item Purchased    Name
Store 1  22.5       Dog Food   Chris
Store 2   2.5   Kitty Litter  Kelvin
Store 1   5.0      Bird Seed   Vinod

1. 创建DataFrame(例子三)
2. 查询 dataframe 的index：df.loc['index']
   

>>> df.loc['Store 2']
Cost                       2.5
Item Purchased    Kitty Litter
Name                    Kelvin
Name: Store 2, dtype: object

1. df.loc['Store 2']

>>> df.loc['Store 1']
         Cost Item Purchased   Name
Store 1  22.5       Dog Food  Chris
Store 1   5.0      Bird Seed  Vinod

1. df.loc['Store 1']

>>> df['Item Purchased']
Store 1        Dog Food
Store 2    Kitty Litter
Store 1       Bird Seed
Name: Item Purchased, dtype: object

1. df['Item Purchased']
2. 查 store1 的 cost 是多少
   

>>> df.loc['Store 1', 'Cost']
Store 1    22.5
Store 1     5.0
Name: Cost, dtype: float64

1. df.loc['Store 1', 'Cost']
2. 查询Cost大于3的Name
   

>>> df['Name'][df['Cost']>3]
Store 1    Chris
Store 1    Vinod
Name: Name, dtype: object

1. df['Name'][df['Cost']>3]
2. 查询DataFrame 的类型
   

>>> type(df.loc['Store 2'])
<class 'pandas.core.series.Series'>

1. type( )例子
2. drop dataframe (但这不会把原来的 dataframe drop 掉)
   

>>> df.drop('Store 1')
         Cost Item Purchased    Name
Store 2   2.5   Kitty Litter  Kelvin

>>> df
         Cost Item Purchased    Name
Store 1  22.5       Dog Food   Chris
Store 2   2.5   Kitty Litter  Kelvin
Store 1   5.0      Bird Seed   Vinod

1. df.drop('Store 1')

>>> copy_df = df.copy()
>>> copy_df
         Cost Item Purchased    Name
Store 1  22.5       Dog Food   Chris
Store 2   2.5   Kitty Litter  Kelvin
Store 1   5.0      Bird Seed   Vinod
>>> copy_df = df.drop('Store 1')
>>> copy_df
         Cost Item Purchased    Name
Store 2   2.5   Kitty Litter  Kelvin

1. 把dataframe数据drop的例子

也可以用 del 把 Column 列删除掉

>>> del copy_df['Name']
>>> copy_df
         Cost Item Purchased
Store 2   2.5   Kitty Litter

1. del copy_df['Name']
2. set_index
3. rename column
4. 可以修改dataframe里的数据
   

>>> df = pd.DataFrame([purchase_1,purchase_2,purchase_3],index=['Store 1','Store 2','Store 1'])
>>> df
         Cost Item Purchased    Name
Store 1  22.5       Dog Food   Chris
Store 2   2.5   Kitty Litter  Kelvin
Store 1   5.0      Bird Seed   Vinod

>>> df['Cost'] = df['Cost'] * 0.8
>>> df
         Cost Item Purchased    Name
Store 1  18.0       Dog Food   Chris
Store 2   2.0   Kitty Litter  Kelvin
Store 1   4.0      Bird Seed   Vinod

1. df['Cost'] * 0.8

>>> df = pd.DataFrame([purchase_1,purchase_2,purchase_3],index=['Store 1','Store 2','Store 1'])
>>> costs = df['Cost']
>>> costs
Store 1    22.5
Store 2     2.5
Store 1     5.0
Name: Cost, dtype: float64
>>> costs += 2
>>> costs
Store 1    24.5
Store 2     4.5
Store 1     7.0
Name: Cost, dtype: float64

1. costs = df['Cost']

进阶操作

1. Merge
   Full Outer Join
   Inner Join
   Left Join
   Right Join
2. apply
3. group by
4. agg
5. astype
6. cut
   

s = pd.Series([168, 180, 174, 190, 170, 185, 179, 181, 175, 169, 182, 177, 180, 171])
pd.cut(s, 3)
pd.cut(s, 3, labels=['Small', 'Medium', 'Large'])

1. cut( )
2. pivot table 

 

Date in DataFrame

1. Timestampe
2. period
3. DatetimeINdex
4. PeriodIndex
5. to_datetime
6. Timedelta
7. date_range
8. difference between date value
9. resample
10. asfreq - changing the frequency of the date

 

读取 csv 文件

import pandas as pd
pd.read_csv('student.csv')

1. 读取csv
   

>>> from pandas import Series, DataFrame
>>> import pandas as pd
>>> df_student = pd.read_csv('student.csv')
>>> df_student
        name   class  marks  age
    janice  python     80   22
      alex  python     95   21
     peter  python     85   25
       ken    java     75   28
 lawerance    java     50   22

1. pd.read_csv('student.csv')(例子一)

df_student = pd.read_csv('student.csv', index_col=0, skiprows=1)

1. pd.read_csv('student.csv')(例子二)
2. 获取分数大于70的数据
   

>>> df_student['marks'] > 70
    True
    True
    True
    True
   False
Name: marks, dtype: bool

1. 方法一: df_student['marks'] > 70

>>> df_student.where(df_student['marks']>70)
     name   class  marks   age
 janice  python   80.0  22.0
   alex  python   95.0  21.0
  peter  python   85.0  25.0
    ken    java   75.0  28.0
    NaN     NaN    NaN   NaN

1. 方法二: df_student.where(df_student['marks']>70)

>>> df_student[df_student['marks'] > 70]
     name   class  marks  age
0  janice  python     80   22
1    alex  python     95   21
2   peter  python     85   25
3     ken    java     75   28

1. 方法三: df_student[df_student['marks'] > 70]
2. 获取class = 'python' 的数据，df.count( ) 是不会把 NaN数据计算在其中
   

>>> df2 = df_student.where(df_student['class'] == 'python') 
>>> df2
     name   class  marks   age
0  janice  python   80.0  22.0
1    alex  python   95.0  21.0
2   peter  python   85.0  25.0
3     NaN     NaN    NaN   NaN
4     NaN     NaN    NaN   NaN

>>> df2 = df_student[df_student['class'] == 'python']
>>> df2
     name   class  marks  age
0  janice  python     80   22
1    alex  python     95   21
2   peter  python     85   25

1. df_student.where( )例子
2. 计算 class 的数目 e.g. count( )
   

>>> df2['class'].count() #不会把 NaN也计算
3

>>> df_student['class'].count() #会把 NaN也计算
5

1. df.count( )例子
2. 删取NaN数据
   

>>> df3 = df2.dropna()
>>> df3
     name   class  marks   age
0  janice  python   80.0  22.0
1    alex  python   95.0  21.0
2   peter  python   85.0  25.0

1. df2.dropna()
2. 获取age大于23 学生的数据
   

>>> df_student
        name   class  marks  age
0     janice  python     80   22
1       alex  python     95   21
2      peter  python     85   25
3        ken    java     75   28
4  lawerance    java     50   22

>>> df_student[df_student['age'] > 23]
    name   class  marks  age
2  peter  python     85   25
3    ken    java     75   28

>>> df_student['age'] > 23
0    False
1    False
2     True
3     True
4    False
Name: age, dtype: bool

>>> len(df_student[df_student['age'] > 23])
2

1. df_student[df_student['age'] > 23]
2. 获取age大于23和分数大于80分学生的数据
   

>>> df_student
        name   class  marks  age
0     janice  python     80   22
1       alex  python     95   21
2      peter  python     85   25
3        ken    java     75   28
4  lawerance    java     50   22
>>> df_and = df_student[(df_student['age'] > 23) & (df_student['marks'] > 80)]
>>> df_and
    name   class  marks  age
2  peter  python     85   25

1. df_student[(df_student['age'] > 23) & (df_student['marks'] > 80)]
2. 获取age大于23或分数大于80分学生的数据
   

>>> df_student
        name   class  marks  age
0     janice  python     80   22
1       alex  python     95   21
2      peter  python     85   25
3        ken    java     75   28
4  lawerance    java     50   22

>>> df_or = df_student[(df_student['age'] > 23) | (df_student['marks'] > 80)]
>>> df_or
    name   class  marks  age
1   alex  python     95   21
2  peter  python     85   25
3    ken    java     75   28

1. df_student[(df_student['age'] > 23) | (df_student['marks'] > 80)]
2. 重新定义index的数值 df.set_index( )
   

>>> df_student = pd.read_csv('student.csv')
>>> df_student
        name   class  marks  age
0     janice  python     80   22
1       alex  python     95   21
2      peter  python     85   25
3        ken    java     75   28
4  lawerance    java     50   22

>>> df_student['order_id'] = df_student.index
>>> df_student
        name   class  marks  age  order_id
0     janice  python     80   22         0
1       alex  python     95   21         1
2      peter  python     85   25         2
3        ken    java     75   28         3
4  lawerance    java     50   22         4

>>> df_student = df_student.set_index('class')
>>> df_student
             name  marks  age  order_id
class                                  
python     janice     80   22         0
python       alex     95   21         1
python      peter     85   25         2
java          ken     75   28         3
java    lawerance     50   22         4

1. df_student.set_index( )例子
2. 获取在 dataframe column 中唯一的数据

>>> df_student = pd.read_csv('student.csv')
>>> df_student['class'].unique()
array(['python', 'java'], dtype=object)

1. df.unique( )例子

 

python 的可视化 matplotlib

1. plot