pandas练习题

关注

原创最新推荐文章于 2026-03-05 01:07:34 发布 3.9k 阅读 AI 写同款· GEO 优化 ›

Pandas基础题一百道（16~20）目录 16、打印DataFrame的前后数据行 ①：打印DataFrame ②：打印DataFrame前10行数据 ③：打印DataFrame后十行数据 17、查看DataFrame的信息和基本数据统计 ①：打印DataFrame ②：查看DataFrame的基本信息 18、统计数据列的值出现的次数 ①：数据 ②：统计数据列的值出现的次数 19、DataFrame前N行存入CSV文件 ①：数据 ②：选取前五行数据方法一：方法二： ③：将筛选的数据存入CSV文... 阅读详情

导入 Pandas 库并简写为 pd，并输出版本号

>>> import pandas as pd
>>> pd.__version__
'1.1.4'

2.从列表创建series

>>> data = [1,2,3,4,5,6]
>>> frame = pd.Series(data, index = ['A','B','C','D','E','F'])
>>> frame
A    1
B    2
C    3
D    4
E    5
F    6
dtype: int64

3.从字典创建series

>>> data = {'A':1, 'B':2, 'C':3, 'D':4}
>>> frame=pd.Series(data)
>>> frame
A    1
B    2
C    3
D    4
dtype: int64

4.从Numpy数组创建DataFrame

>>> import numpy as np
>>> data = np.arange(6).reshape(2,3)
>>> data
array([[0, 1, 2],
       [3, 4, 5]])
>>> frame = pd.DataFrame(data,index=['A','B'],columns=['O','T','C'])
>>> frame
   O  T  C
A  0  1  2
B  3  4  5

5.从csv中创建DataFrame，分隔符为"，"，编码为“utf8”

>>> frame = pd.read_csv(r'C:\Users\MARS\Desktop\测试数据.csv', sep=',',encoding='utf8')

6.从字典对象创建DataFrame，索引设置为labels

>>> import numpy as np
>>> import pandas as pd
>>> data = {'animal':['cat','cat','snake','dog','god','cat','snake','cat','dog','dog'],'age':[2.5,3,0.5,np.nan,5,2,4.5,np.nan,7,3],'visits':[1,3,2,3,2,3,1,1,2,1],'priority':['yes','yes','no','yes','no','no','no','yse','no','no']}
>>> labels = ['a','b','c','d','e','f','g','h','i','j']
>>> frame = pd.DataFrame(data,index = labels)
>>> frame
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no
d    dog  NaN       3      yes
e    god  5.0       2       no
f    cat  2.0       3       no
g  snake  4.5       1       no
h    cat  NaN       1      yse
i    dog  7.0       2       no
j    dog  3.0       1       no

7.显示DataFrame的基础信息，包括行数，列名，值的数量和类型

>>> frame.info()
<class 'pandas.core.frame.DataFrame'>
Index: 10 entries, a to j
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   animal    10 non-null     object 
 1   age       8 non-null      float64
 2   visits    10 non-null     int64  
 3   priority  10 non-null     object 
dtypes: float64(1), int64(1), object(2)
memory usage: 472.0+ bytes

8.展示前三行（两种方式）

>>> frame.iloc[:3]
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no
>>> frame.head(3)
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no

9.取出frame的animal和age列

>>> frame.loc[:,['animal','age']]
  animal  age
a    cat  2.5
b    cat  3.0
c  snake  0.5
d    dog  NaN
e    god  5.0
f    cat  2.0
g  snake  4.5
h    cat  NaN
i    dog  7.0
j    dog  3.0
>>> frame[['animal','age']]
  animal  age
a    cat  2.5
b    cat  3.0
c  snake  0.5
d    dog  NaN
e    god  5.0
f    cat  2.0
g  snake  4.5
h    cat  NaN
i    dog  7.0
j    dog  3.0

10.取出索引为[3, 4, 8]行的animal和age列

>>> frame.loc[frame.index[[3,4,8]],['animal','age']]
  animal  age
d    dog  NaN
e    god  5.0
i    dog  7.0

11.取出age值大于3的行

>>> frame[frame['age']>3]
  animal  age  visits priority
e    god  5.0       2       no
g  snake  4.5       1       no
i    dog  7.0       2       no

12.取出age值缺失的行

>>> frame[frame['age'].isnull()]
  animal  age  visits priority
d    dog  NaN       3      yes
h    cat  NaN       1      yse

13..取出age在2,4间的行（不含）

>>> frame[(frame['age']>2) &( frame['age']<4)]
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
j    dog  3.0       1       no

14.f行的age改为1.5

>>> frame.loc['f','age']=1.5

15..计算visits的总和

>>> frame['visits'].sum()
19

16.计算每个不同种类animal的age的平均数

>>> frame.groupby('animal')['age'].mean()
animal
cat      2.333333
dog      5.000000
god      5.000000
snake    2.500000
Name: age, dtype: float64

17.计算df中每个种类animal的数量

>>> frame.groupby('animal')['animal'].count()
animal
cat      4
dog      3
god      1
snake    2
Name: animal, dtype: int64

>>> frame['animal'].value_counts()
cat      4
dog      3
snake    2
god      1
Name: animal, dtype: int64

18.先按age降序排列，后按visits升序排列

>>> frame.sort_values(by=['age','visits'],ascending=['True','False'])
  animal  age  visits priority
c  snake  0.5       2       no
f    cat  2.0       3       no
a    cat  2.5       1      yes
j    dog  3.0       1       no
b    cat  3.0       3      yes
g  snake  4.5       1       no
e    god  5.0       2       no
i    dog  7.0       2       no
h    cat  NaN       1      yse
d    dog  NaN       3      yes

19.将priority列中的yes, no替换为布尔值True, False

>>> frame['priority'] = frame['priority'].map({'yes':True,'no':False})
>>> frame
  animal  age  visits priority
a    cat  2.5       1     True
b    cat  3.0       3     True
c  snake  0.5       2    False
d    dog  NaN       3     True
e    god  5.0       2    False
f    cat  2.0       3    False
g  snake  4.5       1    False
h    cat  NaN       1      NaN
i    dog  7.0       2    False
j    dog  3.0       1    False

20.将animal列中的snake替换为python

>>> frame['animal'] = frame['animal'].replace('snake','python')
>>> frame
   animal  age  visits priority
a     cat  2.5       1     True
b     cat  3.0       3     True
c  python  0.5       2    False
d     dog  NaN       3     True
e     god  5.0       2    False
f     cat  2.0       3    False
g  python  4.5       1    False
h     cat  NaN       1      NaN
i     dog  7.0       2    False
j     dog  3.0       1    False

21.对每种animal的每种不同数量visits，计算平均age，即，返回一个表格，行是aniaml种类，列是visits数量，表格值是行动物种类列访客数量的平均年龄

>>> frame.groupby(['animal','visits'])['age'].mean()
animal  visits
cat     1         2.5
        3         2.5
dog     1         3.0
        2         7.0
        3         NaN
god     2         5.0
python  1         4.5
        2         0.5
Name: age, dtype: float64
>>> frame.dtypes
animal       object
age         float64
visits        int64
priority     object
dtype: object
>>> frame.age = frame.age.astype(float)
>>> frame.pivot_table(index='animal', columns='visits',values = 'age',aggfunc='mean')
visits    1    2    3
animal               
cat     2.5  NaN  2.5
dog     3.0  7.0  NaN
god     NaN  5.0  NaN
python  4.5  0.5  NaN

22..在frame中插入新行k，然后删除该行

>>> frame.loc['K'] = ['cat',5,2,'no']
>>> frame
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no
d    dog  NaN       3      yes
e    god  5.0       2       no
f    cat  2.0       3       no
g  snake  4.5       1       no
h    cat  NaN       1      yse
i    dog  7.0       2       no
j    dog  3.0       1       no
K    cat  5.0       2       no
>>> frame.drop('K')
  animal  age  visits priority
a    cat  2.5       1      yes
b    cat  3.0       3      yes
c  snake  0.5       2       no
d    dog  NaN       3      yes
e    god  5.0       2       no
f    cat  2.0       3       no
g  snake  4.5       1       no
h    cat  NaN       1      yse
i    dog  7.0       2       no
j    dog  3.0       1       no

23.有一列整数列A的DatraFrame，删除数值重复的行

>>> df = pd.DataFrame({'A':[1,2,2,3,4,5,5,5,6,6,7,7]})
>>> df
    A
0   1
1   2
2   2
3   3
4   4
5   5
6   5
7   5
8   6
9   6
10  7
11  7
>>> df1 = df.loc[df['A'].shift() != df['A']]
>>> df1
    A
0   1
1   2
3   3
4   4
5   5
8   6
10  7
>>> df1 = df.drop_duplicates(subset='A')
>>> df1
    A
0   1
1   2
3   3
4   4
5   5
8   6
10  7
>>>

24.一个全数值DatraFrame，每个数字减去该行的平均数

>>> df = pd.DataFrame(np.random.random(size=(5, 3)))

>>> df
          0         1         2
0  0.679254  0.007655  0.312684
1  0.494291  0.800074  0.506416
2  0.518933  0.822710  0.250825
3  0.748430  0.201992  0.227088
4  0.247771  0.044589  0.569569
>>> df1=df.sub(df.mean(axis=1), axis=0)
>>> df1
          0         1         2
0  0.346057 -0.325543 -0.020514
1 -0.105969  0.199814 -0.093845
2 -0.011890  0.291887 -0.279997
3  0.355926 -0.190511 -0.165415
4 -0.039538 -0.242720  0.282259

收录于