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

In [63]:

import pandas as pd
pd.__version__

Out[63]:

'0.24.2'

2.从列表创建 Series

In [3]:

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

Out[3]:

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

3.从字典创建 Series

In [4]:

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

Out[4]:

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

4.从 NumPy 数组创建 DataFrame

In [5]:

dates = pd.date_range('today',periods=6) # 定义时间序列作为 index
num_arr = np.random.randn(6,4) # 传入 numpy 随机数组
columns = ['A','B','C','D'] # 将列表作为列名
df1 = pd.DataFrame(num_arr, index = dates, columns = columns)
df1

Out[5]:

	A	B	C	D
2020-02-04 05:36:17.273759	0.560268	-0.559426	1.176857	0.885549
2020-02-05 05:36:17.273759	2.396094	-0.720063	-1.144393	-0.149686
2020-02-06 05:36:17.273759	0.036016	-1.032553	0.526661	0.524164
2020-02-07 05:36:17.273759	0.120952	-0.495401	-0.006828	-2.375663
2020-02-08 05:36:17.273759	1.125484	0.685709	-0.144614	-0.398538
2020-02-09 05:36:17.273759	-0.027859	0.331943	-1.256073	-0.659318

5.从CSV中创建 DataFrame，分隔符为;，编码格式为gbk

In [6]:

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

6.从字典对象data创建DataFrame，设置索引为labels

In [70]:

import numpy as np
​
data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', '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', 'yes', 'no', 'no']}
​
labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']

In [45]:

df = pd.DataFrame(data, index=labels)
df

Out[45]:

	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	dog	5.0	2	no
f	cat	2.0	3	no
g	snake	4.5	1	no
h	cat	NaN	1	yes
i	dog	7.0	2	no
j	dog	3.0	1	no

7.显示DataFrame的基础信息，包括行的数量；列名；每一列值的数量、类型

In [46]:

df.info()
# 方法二
# df.describe()
<class 'pandas.core.frame.DataFrame'>
Index: 10 entries, a to j
Data columns (total 4 columns):
animal      10 non-null object
age         8 non-null float64
visits      10 non-null int64
priority    10 non-null object
dtypes: float64(1), int64(1), object(2)
memory usage: 400.0+ bytes

8.展示df的前3行

In [47]:

df.iloc[:3]
# 方法二
#df.head(3)

Out[47]:

	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df的animal和age列

In [48]:

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

Out[48]:

	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

In [49]:

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

Out[49]:

	animal	age
d	dog	NaN
e	dog	5.0
i	dog	7.0

11.取出age值大于3的行

In [50]:

df[df['age'] > 3]

Out[50]:

	animal	age	visits	priority
e	dog	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

In [51]:

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

Out[51]:

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

13.取出age在2,4间的行（不含）

In [52]:

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

Out[52]:

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

14.f行的age改为1.5

In [53]:

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

15.计算visits的总和

In [54]:

df['visits'].sum()

Out[54]:

19

16.计算每个不同种类animal的age的平均数

In [55]:

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

Out[55]:

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

17.计算df中每个种类animal的数量

In [56]:

df['animal'].value_counts()

Out[56]:

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

18.先按age降序排列，后按visits升序排列

In [57]:

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

Out[57]:

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

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

In [58]:

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

Out[58]:

	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	dog	5.0	2	False
f	cat	1.5	3	False
g	snake	4.5	1	False
h	cat	NaN	1	True
i	dog	7.0	2	False
j	dog	3.0	1	False

20.将animal列中的snake替换为python

In [59]:

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

Out[59]:

	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	dog	5.0	2	False
f	cat	1.5	3	False
g	python	4.5	1	False
h	cat	NaN	1	True
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

In [75]:

df.dtypes

Out[75]:

animal       object
age         float64
visits       object
priority      int64
dtype: object

In [74]:

df.age=df.age.astype(float)

In [76]:

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

Out[76]:

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

22.在df中插入新行k，然后删除该行

In [71]:

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

Out[71]:

	animal	age	visits	priority
a	cat	2.5	1	1
b	cat	3	3	1
c	python	0.5	2	0
d	dog	NaN	3	1
e	dog	5	2	0
f	cat	1.5	3	0
g	python	4.5	1	0
h	cat	NaN	1	1
i	dog	7	2	0
j	dog	3	1	0

进阶操作

23.有一列整数列A的DatraFrame，删除数值重复的行

In [56]:

df = pd.DataFrame({'A': [1, 2, 2, 3, 4, 5, 5, 5, 6, 7, 7]})
print(df)
df1 = df.loc[df['A'].shift() != df['A']]
# 方法二
# df1 = df.drop_duplicates(subset='A')
print(df1)
    A
0   1
1   2
2   2
3   3
4   4
5   5
6   5
7   5
8   6
9   7
10  7
   A
0  1
1  2
3  3
4  4
5  5
8  6
9  7

24.一个全数值DatraFrame，每个数字减去该行的平均数

In [57]:

df = pd.DataFrame(np.random.random(size=(5, 3)))
print(df)
df1 = df.sub(df.mean(axis=1), axis=0)
print(df1)
          0         1         2
0  0.761859  0.579139  0.023214
1  0.520961  0.847583  0.044559
2  0.186740  0.561425  0.158097
3  0.606828  0.680284  0.903810
4  0.474712  0.404921  0.776503
          0         1         2
0  0.307122  0.124402 -0.431524
1  0.049927  0.376549 -0.426476
2 -0.115347  0.259337 -0.143990
3 -0.123479 -0.050023  0.173503
4 -0.077333 -0.147124  0.224457

25.一个有5列的DataFrame，求哪一列的和最小

In [46]:

df = pd.DataFrame(np.random.random(size=(5, 5)), columns=list('abcde'))
print(df)
df.sum().idxmin()
          a         b         c         d         e
0  0.832332  0.947136  0.614947  0.162827  0.621645
1  0.786207  0.974123  0.675870  0.633438  0.960480
2  0.141939  0.070910  0.963475  0.055656  0.293488
3  0.279569  0.690073  0.570594  0.680619  0.706241
4  0.237010  0.145196  0.295304  0.628794  0.924545

Out[46]:

'd'

26.给定DataFrame，求A列每个值的前3大的B的和

In [35]:

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

In [67]:

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

In [71]:

df = pd.DataFrame({'X': [7, 2, 0, 3, 4, 2, 5, 0, 3, 4]})
​
izero = np.r_[-1, (df['X'] == 0).to_numpy().nonzero()[0]] # 标记0的位置
idx = np.arange(len(df))
df['Y'] = idx - izero[np.searchsorted(izero - 1, idx) - 1]
print(df)
​
# 方法二
# x = (df['X'] != 0).cumsum()
# y = x != x.shift()
# df['Y'] = y.groupby((y != y.shift()).cumsum()).cumsum()
​
# 方法三
# df['Y'] = df.groupby((df['X'] == 0).cumsum()).cumcount()
#first_zero_idx = (df['X'] == 0).idxmax()
# df['Y'].iloc[0:first_zero_idx] += 1
   X  Y
0  7  1
1  2  2
2  0  0
3  3  1
4  4  2
5  2  3
6  5  4
7  0  0
8  3  1
9  4  2

29.一个全数值的DataFrame，返回最大3值的坐标

In [73]:

df = pd.DataFrame(np.random.random(size=(5, 3)))
print(df)
df.unstack().sort_values()[-3:].index.tolist()
          0         1         2
0  0.322047  0.508559  0.481098
1  0.625304  0.582052  0.048630
2  0.848465  0.662735  0.038410
3  0.573324  0.664073  0.606389
4  0.920799  0.462395  0.684100

Out[73]:

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

30.给定DataFrame，将负值代替为同组的平均值

In [78]:

df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'), 
                   'vals': [-12,345,3,1,45,14,4,-52,54,23,-235,21,57,3,87]})
print(df)
​
def replace(group):
    mask = group<0
    group[mask] = group[~mask].mean()
    return group
​
df['vals'] = df.groupby(['grps'])['vals'].transform(replace)
print(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

In [79]:

df = pd.DataFrame({'group': list('aabbabbbabab'),
                    'value': [1, 2, 3, np.nan, 2, 3, np.nan, 1, 7, 3, np.nan, 8]})
print(df)
​
g1 = df.groupby(['group'])['value']
g2 = df.fillna(0).groupby(['group'])['value'] 
​
s = g2.rolling(3, min_periods=1).sum() / g1.rolling(3, min_periods=1).count()
​
s.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

Out[79]:

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

In [82]:

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

Out[82]:

2015-01-01    0.542640
2015-01-02    0.843010
2015-01-05    0.335675
2015-01-06    0.823544
2015-01-07    0.416880
2015-01-08    0.587211
2015-01-09    0.805899
2015-01-12    0.824835
2015-01-13    0.639243
2015-01-14    0.406859
Freq: B, dtype: float64

33.所有礼拜三的值求和

In [83]:

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

Out[83]:

24.085656574156896

34.求每个自然月的平均数

In [84]:

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

Out[84]:

2015-01-31    0.507893
2015-02-28    0.589465
2015-03-31    0.622884
2015-04-30    0.559272
2015-05-31    0.568332
2015-06-30    0.469618
2015-07-31    0.396087
2015-08-31    0.409175
2015-09-30    0.521630
2015-10-31    0.558678
2015-11-30    0.497789
2015-12-31    0.460043
Freq: M, dtype: float64

35.每连续4个月为一组，求最大值所在的日期

In [87]:

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

Out[87]:

2015-01-31   2015-01-02
2015-05-31   2015-03-25
2015-09-30   2015-06-09
2016-01-31   2015-10-01
Freq: 4M, dtype: datetime64[ns]

36.创建2015-2016每月第三个星期四的序列

In [88]:

pd.date_range('2015-01-01', '2016-12-31', freq='WOM-3THU')

Out[88]:

DatetimeIndex(['2015-01-15', '2015-02-19', '2015-03-19', '2015-04-16',
               '2015-05-21', '2015-06-18', '2015-07-16', '2015-08-20',
               '2015-09-17', '2015-10-15', '2015-11-19', '2015-12-17',
               '2016-01-21', '2016-02-18', '2016-03-17', '2016-04-21',
               '2016-05-19', '2016-06-16', '2016-07-21', '2016-08-18',
               '2016-09-15', '2016-10-20', '2016-11-17', '2016-12-15'],
              dtype='datetime64[ns]', freq='WOM-3THU')

数据清洗

In [60]:

df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', 
                               'Budapest_PaRis', 'Brussels_londOn'],
              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],
              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],
                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', 
                               '12. Air France', '"Swiss Air"']})
df

Out[60]:

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

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

In [61]:

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

Out[61]:

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

38.将From_To列从_分开，分成From, To两列，并删除原始列

In [62]:

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

Out[62]:

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

39.将From, To大小写统一

In [63]:

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

Out[63]:

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

In [107]:

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

Out[107]:

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

In [108]:

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

Out[108]:

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

In [39]:

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

Out[39]:

A  0    0.867828
   1    0.633680
   2    0.328333
   3    0.726590
B  0    0.972693
   1    0.140361
   2    0.905836
   3    0.072299
C  0    0.787910
   1    0.408023
   2    0.532220
   3    0.219010
dtype: float64

43.检查s是否是字典顺序排序的

In [40]:

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

Out[40]:

True

44.选择二级索引为1, 3的行

In [41]:

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

Out[41]:

A  1    0.633680
   3    0.726590
B  1    0.140361
   3    0.072299
C  1    0.408023
   3    0.219010
dtype: float64

45.对s进行切片操作，取一级索引从头至B，二级索引从2开始到最后

In [42]:

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

Out[42]:

A  2    0.328333
   3    0.726590
B  2    0.905836
   3    0.072299
dtype: float64

46.计算每个一级索引的和（A, B, C每一个的和）

In [43]:

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

Out[43]:

A    2.556431
B    2.091189
C    1.947163
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

In [44]:

new_s = s.swaplevel(0, 1)
print(new_s)
print(new_s.index.is_lexsorted())
new_s = new_s.sort_index()
print(new_s)
0  A    0.867828
1  A    0.633680
2  A    0.328333
3  A    0.726590
0  B    0.972693
1  B    0.140361
2  B    0.905836
3  B    0.072299
0  C    0.787910
1  C    0.408023
2  C    0.532220
3  C    0.219010
dtype: float64
False
0  A    0.867828
   B    0.972693
   C    0.787910
1  A    0.633680
   B    0.140361
   C    0.408023
2  A    0.328333
   B    0.905836
   C    0.532220
3  A    0.726590
   B    0.072299
   C    0.219010
dtype: float64

可视化

In [18]:

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

48.画出df的散点图

In [19]:

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

Out[19]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fbb753a8eb8>

49.可视化指定4维DataFrame

In [20]:

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

Out[20]:

<matplotlib.axes._subplots.AxesSubplot at 0x7fbb752f5780>

 

50.在同一个图中可视化2组数据，共用X轴，但y轴不同

In [25]:

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