Pandas50

May 24, 2026 · 12 分钟阅读 · Python Pandas ·

title: "Pandas练习题50题" date: 2026-05-24T14:09:49Z

Pandas练习题50题

作者：王大毛，和鲸社区

出处：https://www.kesci.com/home/project/5ddc974ef41512002cec1dca

修改：黄海广

Pandas 是基于 NumPy 的一种数据处理工具，该工具为了解决数据分析任务而创建。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的函数和方法。这些练习着重DataFrame和Series对象的基本操作，包括数据的索引、分组、统计和清洗。

基本操作

1.导入 Pandas 库并简写为 pd，并输出版本号

1import pandas as pd
2pd.__version__

'0.22.0'

2. 从列表创建 Series

1arr = [0, 1, 2, 3, 4]
2df = pd.Series(arr) # 如果不指定索引，则默认从 0 开始
3df

0    0
1    1
2    2
3    3
4    4
dtype: int64

3. 从字典创建 Series

1d = {'a':1,'b':2,'c':3,'d':4,'e':5}
2df = pd.Series(d)
3df

a    1
b    2
c    3
d    4
e    5
dtype: int64

4. 从 NumPy 数组创建 DataFrame

1import numpy as np
2dates = pd.date_range('today', periods=6)  # 定义时间序列作为 index
3num_arr = np.random.randn(6, 4)  # 传入 numpy 随机数组
4columns = ['A', 'B', 'C', 'D']  # 将列表作为列名
5df = pd.DataFrame(num_arr, index=dates, columns=columns)
6df

	A	B	C	D
2020-01-10 22:46:01.642021	0.277099	0.665053	0.882637	-0.598895
2020-01-11 22:46:01.642021	0.365233	-2.529804	-0.699849	0.159623
2020-01-12 22:46:01.642021	-0.831850	-2.099049	-0.976407	-0.342800
2020-01-13 22:46:01.642021	0.680800	1.682999	0.144469	-2.503013
2020-01-14 22:46:01.642021	-0.413880	0.876169	-1.047877	0.996865
2020-01-15 22:46:01.642021	1.373956	0.029732	-0.549268	-0.287584

5. 从CSV中创建 DataFrame，分隔符为“；”，编码格式为gbk

df = pd.read_csv('test.csv', encoding='gbk', sep=';')

6. 从字典对象创建DataFrame，并设置索引

 1import numpy as np
 2data = {
 3    'animal':
 4    ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
 5    'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
 6    'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
 7    'priority':
 8    ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']
 9}
10
11labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
12df = pd.DataFrame(data, index=labels)
13df

	age	animal	priority	visits
a	2.5	cat	yes	1
b	3.0	cat	yes	3
c	0.5	snake	no	2
d	NaN	dog	yes	3
e	5.0	dog	no	2
f	2.0	cat	no	3
g	4.5	snake	no	1
h	NaN	cat	yes	1
i	7.0	dog	no	2
j	3.0	dog	no	1

7. 显示df的基础信息，包括行的数量；列名；每一列值的数量、类型

1df.info()
2# 方法二
3# df.describe()

<class 'pandas.core.frame.DataFrame'>
Index: 10 entries, a to j
Data columns (total 4 columns):
age         8 non-null float64
animal      10 non-null object
priority    10 non-null object
visits      10 non-null int64
dtypes: float64(1), int64(1), object(2)
memory usage: 400.0+ bytes

8. 展示df的前3行

1df.iloc[:3]
2# 方法二
3#df.head(3)

	age	animal	priority	visits
a	2.5	cat	yes	1
b	3.0	cat	yes	3
c	0.5	snake	no	2

9. 取出df的animal和age列

1df.loc[:, ['animal', 'age']]
2# 方法二
3# df[['animal', 'age']]

	animal	age
a	cat	2.5
b	cat	3.0
c	snake	0.5
d	dog	NaN
e	dog	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

1df.loc[df.index[[3, 4, 8]], ['animal', 'age']]

	animal	age
d	dog	NaN
e	dog	5.0
i	dog	7.0

11. 取出age值大于3的行

1df[df['age'] > 3]

	age	animal	priority	visits
e	5.0	dog	no	2
g	4.5	snake	no	1
i	7.0	dog	no	2

12. 取出age值缺失的行

1df[df['age'].isnull()]

	age	animal	priority	visits
d	NaN	dog	yes	3
h	NaN	cat	yes	1

13.取出age在2,4间的行（不含）

1df[(df['age']>2) & (df['age']>4)]
2# 方法二
3# df[df['age'].between(2, 4)]

	age	animal	priority	visits
e	5.0	dog	no	2
g	4.5	snake	no	1
i	7.0	dog	no	2

14. f行的age改为1.5

1df.loc['f', 'age'] = 1.5

15. 计算visits的总和

1df['visits'].sum()

16. 计算每个不同种类animal的age的平均数

1df.groupby('animal')['age'].mean()

animal
cat      2.333333
dog      5.000000
snake    2.500000
Name: age, dtype: float64

17. 在df中插入新行k，然后删除该行

1#插入
2df.loc['k'] = [5.5, 'dog', 'no', 2]
3# 删除
4df = df.drop('k')
5df

	age	animal	priority	visits
a	2.5	cat	yes	1
b	3.0	cat	yes	3
c	0.5	snake	no	2
d	NaN	dog	yes	3
e	5.0	dog	no	2
f	1.5	cat	no	3
g	4.5	snake	no	1
h	NaN	cat	yes	1
i	7.0	dog	no	2
j	3.0	dog	no	1

18. 计算df中每个种类animal的数量

1df['animal'].value_counts()

dog      4
cat      4
snake    2
Name: animal, dtype: int64

19. 先按age降序排列，后按visits升序排列

1df.sort_values(by=['age', 'visits'], ascending=[False, True])

	age	animal	priority	visits
i	7.0	dog	no	2
e	5.0	dog	no	2
g	4.5	snake	no	1
j	3.0	dog	no	1
b	3.0	cat	yes	3
a	2.5	cat	yes	1
f	1.5	cat	no	3
c	0.5	snake	no	2
h	NaN	cat	yes	1
d	NaN	dog	yes	3

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

1df['priority'] = df['priority'].map({'yes': True, 'no': False})
2df

	age	animal	priority	visits
a	2.5	cat	True	1
b	3.0	cat	True	3
c	0.5	snake	False	2
d	NaN	dog	True	3
e	5.0	dog	False	2
f	1.5	cat	False	3
g	4.5	snake	False	1
h	NaN	cat	True	1
i	7.0	dog	False	2
j	3.0	dog	False	1

21. 将animal列中的snake替换为python

1df['animal'] = df['animal'].replace('snake', 'python')
2df

	age	animal	priority	visits
a	2.5	cat	True	1
b	3.0	cat	True	3
c	0.5	python	False	2
d	NaN	dog	True	3
e	5.0	dog	False	2
f	1.5	cat	False	3
g	4.5	python	False	1
h	NaN	cat	True	1
i	7.0	dog	False	2
j	3.0	dog	False	1

22. 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

1df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')

visits	1	2	3
animal
cat	2.5	NaN	2.25
dog	3.0	6.0	NaN
python	4.5	0.5	NaN

进阶操作

23. 有一列整数列A的DatraFrame，删除数值重复的行

1df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})
2print(df)
3df1 = df.loc[df['A'].shift() != df['A']]
4# 方法二
5# df1 = df.drop_duplicates(subset='A')
6print(df1)

24. 一个全数值DatraFrame，每个数字减去该行的平均数

1df = pd.DataFrame(np.random.random(size=(5, 3)))
2print(df)
3df1 = df.sub(df.mean(axis=1), axis=0)
4print(df1)

          0         1         2
0  0.465407  0.152497  0.861174
1  0.623682  0.627339  0.495652
2  0.835176  0.862376  0.693047
3  0.319698  0.306709  0.654063
4  0.234855  0.194232  0.438597
          0         1         2
0 -0.027619 -0.340529  0.368148
1  0.041457  0.045115 -0.086572
2  0.038310  0.065509 -0.103819
3 -0.107125 -0.120114  0.227239
4 -0.054373 -0.094996  0.149368

25. 一个有5列的DataFrame，求哪一列的和最小

1df = pd.DataFrame(np.random.random(size=(5, 5)), columns=list('abcde'))
2print(df)
3df.sum().idxmin()

          a         b         c         d         e
0  0.653658  0.730994  0.223025  0.456730  0.288283
1  0.937546  0.640995  0.197359  0.671524  0.006035
2  0.392762  0.174955  0.053928  0.318634  0.464534
3  0.741499  0.197861  0.988105  0.633780  0.914250
4  0.469285  0.309043  0.162127  0.032480  0.863017





'c'

26. 给定DataFrame，求A列每个值的前3大的B的值的和

1df = pd.DataFrame({'A': list('aaabbcaabcccbbc'), 
2                   'B': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})
3print(df)
4df1 = df.groupby('A')['B'].nlargest(3).sum(level=0)
5print(df1)

    A    B
0   a   12
1   a  345
2   a    3
3   b    1
4   b   45
5   c   14
6   a    4
7   a   52
8   b   54
9   c   23
10  c  235
11  c   21
12  b   57
13  b    3
14  c   87
A
a    409
b    156
c    345
Name: B, dtype: int64

27. 给定DataFrame，有列A, B，A的值在1-100（含），对A列每10步长，求对应的B的和

1df = pd.DataFrame({
2    'A': [1, 2, 11, 11, 33, 34, 35, 40, 79, 99],
3    'B': [1, 2, 11, 11, 33, 34, 35, 40, 79, 99]
4})
5print(df)
6df1 = df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()
7print(df1)

    A   B
0   1   1
1   2   2
2  11  11
3  11  11
4  33  33
5  34  34
6  35  35
7  40  40
8  79  79
9  99  99
A
(0, 10]        3
(10, 20]      22
(20, 30]       0
(30, 40]     142
(40, 50]       0
(50, 60]       0
(60, 70]       0
(70, 80]      79
(80, 90]       0
(90, 100]     99
Name: B, dtype: int64

28. 给定DataFrame，计算每个元素至左边最近的0（或者至开头）的距离，生成新列y

1df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})
2# 方法一
3x = (df['X'] != 0).cumsum()
4y = x != x.shift()
5df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()
6print(df)

1# 方法二
2df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()
3first_zero_idx = (df['X'] == 0).idxmax()
4df['Y'].iloc[0:first_zero_idx] += 1
5print(df)

29. 一个全数值的DataFrame，返回最大3个值的坐标

1df = pd.DataFrame(np.random.random(size=(5, 3)))
2print(df)
3df.unstack().sort_values()[-3:].index.tolist()

          0         1         2
0  0.974321  0.454025  0.018815
1  0.323491  0.468609  0.834424
2  0.340960  0.826835  0.503252
3  0.812414  0.202745  0.965168
4  0.633172  0.270281  0.915212





[(2, 4), (2, 3), (0, 0)]

30. 给定DataFrame，将负值代替为同组的平均值

 1df = pd.DataFrame({
 2    'grps':
 3    list('aaabbcaabcccbbc'),
 4    'vals': [-12, 345, 3, 1, 45, 14, 4, -52, 54, 23, -235, 21, 57, 3, 87]
 5})
 6print(df)
 7
 8
 9def replace(group):
10    mask = group < 0
11    group[mask] = group[~mask].mean()
12    return group
13
14
15df['vals'] = df.groupby(['grps'])['vals'].transform(replace)
16print(df)

   grps  vals
0     a   -12
1     a   345
2     a     3
3     b     1
4     b    45
5     c    14
6     a     4
7     a   -52
8     b    54
9     c    23
10    c  -235
11    c    21
12    b    57
13    b     3
14    c    87
   grps        vals
0     a  117.333333
1     a  345.000000
2     a    3.000000
3     b    1.000000
4     b   45.000000
5     c   14.000000
6     a    4.000000
7     a  117.333333
8     b   54.000000
9     c   23.000000
10    c   36.250000
11    c   21.000000
12    b   57.000000
13    b    3.000000
14    c   87.000000

31. 计算3位滑动窗口的平均值，忽略NAN

 1df = pd.DataFrame({
 2    'group': list('aabbabbbabab'),
 3    'value': [1, 2, 3, np.nan, 2, 3, np.nan, 1, 7, 3, np.nan, 8]
 4})
 5print(df)
 6
 7g1 = df.groupby(['group'])['value']
 8g2 = df.fillna(0).groupby(['group'])['value']
 9
10s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()
11s.reset_index(level=0, drop=True).sort_index()

   group  value
0      a    1.0
1      a    2.0
2      b    3.0
3      b    NaN
4      a    2.0
5      b    3.0
6      b    NaN
7      b    1.0
8      a    7.0
9      b    3.0
10     a    NaN
11     b    8.0





0     1.000000
1     1.500000
2     3.000000
3     3.000000
4     1.666667
5     3.000000
6     3.000000
7     2.000000
8     3.666667
9     2.000000
10    4.500000
11    4.000000
Name: value, dtype: float64

Series 和 Datetime索引

32. 创建Series s，将2015所有工作日作为随机值的索引

1dti = pd.date_range(start='2015-01-01', end='2015-12-31', freq='B') 
2s = pd.Series(np.random.rand(len(dti)), index=dti)
3
4s.head(10)

2015-01-01    0.503458
2015-01-02    0.194185
2015-01-05    0.550930
2015-01-06    0.174309
2015-01-07    0.316911
2015-01-08    0.288385
2015-01-09    0.293285
2015-01-12    0.340436
2015-01-13    0.630009
2015-01-14    0.076130
Freq: B, dtype: float64

33. 所有礼拜三的值求和

1s[s.index.weekday == 2].sum()

27.272318047689705

34. 求每个自然月的平均数

1s.resample('M').mean()

2015-01-31    0.375417
2015-02-28    0.551560
2015-03-31    0.540772
2015-04-30    0.450957
2015-05-31    0.369119
2015-06-30    0.588625
2015-07-31    0.584358
2015-08-31    0.609751
2015-09-30    0.511285
2015-10-31    0.555546
2015-11-30    0.528777
2015-12-31    0.574317
Freq: M, dtype: float64

35. 每连续4个月为一组，求最大值所在的日期

1s.groupby(pd.Grouper(freq='4M')).idxmax()

2015-01-31   2015-01-15
2015-05-31   2015-02-04
2015-09-30   2015-06-02
2016-01-31   2015-12-08
dtype: datetime64[ns]

36. 创建2015-2016每月第三个星期四的序列

1pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')
2#数据清洗
3df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', 
4                               'Budapest_PaRis', 'Brussels_londOn'],
5              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],
6              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],
7                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', 
8                               '12. Air France', '"Swiss Air"']})
9df

	Airline	FlightNumber	From_To	RecentDelays
0	KLM(!)	10045.0	LoNDon_paris	[23, 47]
1	<Air France> (12)	NaN	MAdrid_miLAN	[]
2	(British Airways. )	10065.0	londON_StockhOlm	[24, 43, 87]
3	12. Air France	NaN	Budapest_PaRis	[13]
4	"Swiss Air"	10085.0	Brussels_londOn	[67, 32]

37. FlightNumber列中有些值缺失了，他们本来应该是每一行增加10，填充缺失的数值，并且令数据类型为整数

1df['FlightNumber'] = df['FlightNumber'].interpolate().astype(int)
2df

	Airline	FlightNumber	From_To	RecentDelays
0	KLM(!)	10045	LoNDon_paris	[23, 47]
1	<Air France> (12)	10055	MAdrid_miLAN	[]
2	(British Airways. )	10065	londON_StockhOlm	[24, 43, 87]
3	12. Air France	10075	Budapest_PaRis	[13]
4	"Swiss Air"	10085	Brussels_londOn	[67, 32]

38. 将From_To列从_分开，分成From, To两列，并删除原始列

1temp = df.From_To.str.split('_', expand=True)
2temp.columns = ['From', 'To']
3df = df.join(temp)
4df = df.drop('From_To', axis=1)
5df

	Airline	FlightNumber	RecentDelays	From	To
0	KLM(!)	10045	[23, 47]	LoNDon	paris
1	<Air France> (12)	10055	[]	MAdrid	miLAN
2	(British Airways. )	10065	[24, 43, 87]	londON	StockhOlm
3	12. Air France	10075	[13]	Budapest	PaRis
4	"Swiss Air"	10085	[67, 32]	Brussels	londOn

39. 将From, To大小写统一首字母大写其余小写

1df['From'] = df['From'].str.capitalize()
2df['To'] = df['To'].str.capitalize()
3df

	Airline	FlightNumber	RecentDelays	From	To
0	KLM(!)	10045	[23, 47]	London	Paris
1	<Air France> (12)	10055	[]	Madrid	Milan
2	(British Airways. )	10065	[24, 43, 87]	London	Stockholm
3	12. Air France	10075	[13]	Budapest	Paris
4	"Swiss Air"	10085	[67, 32]	Brussels	London

40. Airline列，有一些多余的标点符号，需要提取出正确的航司名称。举例：'(British Airways. )' 应该改为 'British Airways'.

1df['Airline'] = df['Airline'].str.extract(
2    '([a-zA-Z\s]+)', expand=False).str.strip()
3df

	Airline	FlightNumber	RecentDelays	From	To
0	KLM	10045	[23, 47]	London	Paris
1	Air France	10055	[]	Madrid	Milan
2	British Airways	10065	[24, 43, 87]	London	Stockholm
3	Air France	10075	[13]	Budapest	Paris
4	Swiss Air	10085	[67, 32]	Brussels	London

41. Airline列，数据被以列表的形式录入，但是我们希望每个数字被录入成单独一列，delay_1, delay_2, ...没有的用NAN替代。

1delays = df['RecentDelays'].apply(pd.Series)
2delays.columns = ['delay_{}'.format(n) for n in range(1, len(delays.columns)+1)]
3df = df.drop('RecentDelays', axis=1).join(delays)
4df

	Airline	FlightNumber	From	To	delay_1	delay_2	delay_3
0	KLM	10045	London	Paris	23.0	47.0	NaN
1	Air France	10055	Madrid	Milan	NaN	NaN	NaN
2	British Airways	10065	London	Stockholm	24.0	43.0	87.0
3	Air France	10075	Budapest	Paris	13.0	NaN	NaN
4	Swiss Air	10085	Brussels	London	67.0	32.0	NaN

层次化索引

42. 用 letters = ['A', 'B', 'C']和 numbers = list(range(10))的组合作为系列随机值的层次化索引

1letters = ['A', 'B', 'C']
2numbers = list(range(4))
3
4mi = pd.MultiIndex.from_product([letters, numbers])
5s = pd.Series(np.random.rand(12), index=mi)
6s

A  0    0.250785
   1    0.146978
   2    0.596062
   3    0.064608
B  0    0.709660
   1    0.515778
   2    0.483163
   3    0.524490
C  0    0.360434
   1    0.987620
   2    0.527151
   3    0.636960
dtype: float64

43. 检查s是否是字典顺序排序的

1s.index.is_lexsorted()
2# 方法二
3# s.index.lexsort_depth == s.index.nlevels

True

44. 选择二级索引为1, 3的行

1s.loc[:, [1, 3]]

A  1    0.146978
   3    0.064608
B  1    0.515778
   3    0.524490
C  1    0.987620
   3    0.636960
dtype: float64

45. 对s进行切片操作，取一级索引至B，二级索引从2开始到最后

1s.loc[pd.IndexSlice[:'B', 2:]]
2# 方法二
3# s.loc[slice(None, 'B'), slice(2, None)]

A  2    0.596062
   3    0.064608
B  2    0.483163
   3    0.524490
dtype: float64

46. 计算每个一级索引的和（A, B, C每一个的和）

1s.sum(level=0)
2#方法二
3#s.unstack().sum(axis=0)

A    1.058433
B    2.233091
C    2.512164
dtype: float64

47. 交换索引等级，新的Series是字典顺序吗？不是的话请排序

1new_s = s.swaplevel(0, 1)
2print(new_s)
3print(new_s.index.is_lexsorted())
4new_s = new_s.sort_index()
5print(new_s)

0  A    0.250785
1  A    0.146978
2  A    0.596062
3  A    0.064608
0  B    0.709660
1  B    0.515778
2  B    0.483163
3  B    0.524490
0  C    0.360434
1  C    0.987620
2  C    0.527151
3  C    0.636960
dtype: float64
False
0  A    0.250785
   B    0.709660
   C    0.360434
1  A    0.146978
   B    0.515778
   C    0.987620
2  A    0.596062
   B    0.483163
   C    0.527151
3  A    0.064608
   B    0.524490
   C    0.636960
dtype: float64

1## 可视化
2import matplotlib.pyplot as plt
3df = pd.DataFrame({"xs": [1, 5, 2, 8, 1], "ys": [4, 2, 1, 9, 6]})
4plt.style.use('ggplot')

48. 画出df的散点图

1df.plot.scatter("xs", "ys", color = "black", marker = "x")

<matplotlib.axes._subplots.AxesSubplot at 0x1f188ddacc0>

49. 可视化指定4维DataFrame

1df = pd.DataFrame({
2    "productivity": [5, 2, 3, 1, 4, 5, 6, 7, 8, 3, 4, 8, 9],
3    "hours_in": [1, 9, 6, 5, 3, 9, 2, 9, 1, 7, 4, 2, 2],
4    "happiness": [2, 1, 3, 2, 3, 1, 2, 3, 1, 2, 2, 1, 3],
5    "caffienated": [0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]
6})
7
8df.plot.scatter(
9    "hours_in", "productivity", s=df.happiness * 100, c=df.caffienated)

<matplotlib.axes._subplots.AxesSubplot at 0x1f18aea4c18>

50. 在同一个图中可视化2组数据，共用X轴，但y轴不同

 1df = pd.DataFrame({
 2    "revenue": [57, 68, 63, 71, 72, 90, 80, 62, 59, 51, 47, 52],
 3    "advertising":
 4    [2.1, 1.9, 2.7, 3.0, 3.6, 3.2, 2.7, 2.4, 1.8, 1.6, 1.3, 1.9],
 5    "month":
 6    range(12)
 7})
 8
 9ax = df.plot.bar("month", "revenue", color="green")
10df.plot.line("month", "advertising", secondary_y=True, ax=ax)
11ax.set_xlim((-1, 12))

(-1, 12)