Python--Pandas入门

其他 2020-08-04 10:26:23 阅读次数: 0

目录

数据结构Series

import numpy as np
import pandas as pd

构造和初始化series

s = pd.Series([7, 'Beijing', 3.14, -12345, 'Hanxiao'])

s[1]

'Beijing'

s

0          7
1    Beijing
2       3.14
3     -12345
4    Hanxiao
dtype: object

pandas默认用0到n作为Series的index，也可以自定义index（索引）。

s = pd.Series([7, 'Beijing', 3.14, -12345, 'Hanxiao'], index=['A', 'B', 'C', 'D', 'E'])

s

A          7
B    Beijing
C       3.14
D     -12345
E    Hanxiao
dtype: object

s['A']

7

s[['A', 'D', 'B']]

A          7
D     -12345
B    Beijing
dtype: object

可以用list构建Series，同时可以指定index。也可以用dict来初始化Series。

cities = {'Beijing':55000, 'Shanghai':60000, 'Shenzhen':50000, 'Hangzhou':30000, 'Guangzhou':40000, 'Suzhou':None}

cities

{'Beijing': 55000,
 'Shanghai': 60000,
 'Shenzhen': 50000,
 'Hangzhou': 30000,
 'Guangzhou': 40000,
 'Suzhou': None}

apt = pd.Series(cities, name='income')

apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     50000.0
Hangzhou     30000.0
Guangzhou    40000.0
Suzhou           NaN
Name: income, dtype: float64

# 索引
apt['Guangzhou']

40000.0

apt[4]

40000.0

apt[1:]

Shanghai     60000.0
Shenzhen     50000.0
Hangzhou     30000.0
Guangzhou    40000.0
Suzhou           NaN
Name: income, dtype: float64

apt[:-1]

Beijing      55000.0
Shanghai     60000.0
Shenzhen     50000.0
Hangzhou     30000.0
Guangzhou    40000.0
Name: income, dtype: float64

apt[[3,4,1]]

Hangzhou     30000.0
Guangzhou    40000.0
Shanghai     60000.0
Name: income, dtype: float64

apt[['Shanghai', 'Shenzhen']]

Shanghai    60000.0
Shenzhen    50000.0
Name: income, dtype: float64

# 简单的计算
3*apt

Beijing      165000.0
Shanghai     180000.0
Shenzhen     150000.0
Hangzhou      90000.0
Guangzhou    120000.0
Suzhou            NaN
Name: income, dtype: float64

apt/2.5

Beijing      22000.0
Shanghai     24000.0
Shenzhen     20000.0
Hangzhou     12000.0
Guangzhou    16000.0
Suzhou           NaN
Name: income, dtype: float64

# 基于索引做计算
apt[1:] + apt[:-1]

Beijing           NaN
Guangzhou     80000.0
Hangzhou      60000.0
Shanghai     120000.0
Shenzhen     100000.0
Suzhou            NaN
Name: income, dtype: float64

# in判断index是否存在
'Hangzhou' in apt

True

'Chongqing' in apt

False

# apt['Chongqing'] 报错
print(apt.get('Chongqing'))

None

print(apt.get('Guangzhou'))

40000.0

booling indexing/条件判断索引

apt>=40000

Beijing       True
Shanghai      True
Shenzhen      True
Hangzhou     False
Guangzhou     True
Suzhou       False
Name: income, dtype: bool

# 条件索引
apt[apt>=40000]

Beijing      55000.0
Shanghai     60000.0
Shenzhen     50000.0
Guangzhou    40000.0
Name: income, dtype: float64

# 统计计算
apt.mean()

47000.0

apt.median()

50000.0

apt.max()

60000.0

apt.min()

30000.0

Series赋值

apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     50000.0
Hangzhou     30000.0
Guangzhou    40000.0
Suzhou           NaN
Name: income, dtype: float64

apt['Shenzhen'] = 70000

apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     70000.0
Hangzhou     30000.0
Guangzhou    40000.0
Suzhou           NaN
Name: income, dtype: float64

# 条件赋值
apt[apt<=40000] = 45000

apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     70000.0
Hangzhou     45000.0
Guangzhou    45000.0
Suzhou           NaN
Name: income, dtype: float64

np.log(apt)

Beijing      10.915088
Shanghai     11.002100
Shenzhen     11.156251
Hangzhou     10.714418
Guangzhou    10.714418
Suzhou             NaN
Name: income, dtype: float64

cars = pd.Series({'Beijing':350000, 'Shanghai':400000, 'Shenzhen':300000, 'Tianjing':200000, 'Guangzhou':250000, 'Chongqing':150000})

cars

Beijing      350000
Shanghai     400000
Shenzhen     300000
Tianjing     200000
Guangzhou    250000
Chongqing    150000
dtype: int64

expense = cars + 10*apt

expense

Beijing       900000.0
Chongqing          NaN
Guangzhou     700000.0
Hangzhou           NaN
Shanghai     1000000.0
Shenzhen     1000000.0
Suzhou             NaN
Tianjing           NaN
dtype: float64

缺失数据处理

apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     70000.0
Hangzhou     45000.0
Guangzhou    45000.0
Suzhou           NaN
Name: income, dtype: float64

apt.notnull()

Beijing       True
Shanghai      True
Shenzhen      True
Hangzhou      True
Guangzhou     True
Suzhou       False
Name: income, dtype: bool

apt.isnull()

Beijing      False
Shanghai     False
Shenzhen     False
Hangzhou     False
Guangzhou    False
Suzhou        True
Name: income, dtype: bool

expense

Beijing       900000.0
Chongqing     900000.0
Guangzhou     700000.0
Hangzhou      900000.0
Shanghai     1000000.0
Shenzhen     1000000.0
Suzhou        900000.0
Tianjing      900000.0
dtype: float64

expense[expense.isnull()] = expense.mean()

expense

Beijing       900000.0
Chongqing     900000.0
Guangzhou     700000.0
Hangzhou      900000.0
Shanghai     1000000.0
Shenzhen     1000000.0
Suzhou        900000.0
Tianjing      900000.0
dtype: float64

数据结构DataFrame

二位数组

data = {'City':['Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen', 'Hanghzhou', 'Chongqing'],
        'year':[2017, 2018, 2017, 2018, 2017, 2017],
        'popu':[2100, 2300, 1000, 700, 500, 500]
       }

pd.DataFrame(data)
City year popu
0 Beijing 2017 2100
1 Shanghai 2018 2300
2 Guangzhou 2017 1000
3 Shenzhen 2018 700
4 Hanghzhou 2017 500
5 Chongqing 2017 500 
pd.DataFrame(data, columns=['year','City','popu'])
year City popu
0 2017 Beijing 2100
1 2018 Shanghai 2300
2 2017 Guangzhou 1000
3 2018 Shenzhen 700
4 2017 Hanghzhou 500
5 2017 Chongqing 500 
pd.DataFrame(data, columns=['year','City','popu'], index=['one','two','three','four','five','six'])
year City popu
one 2017 Beijing 2100
two 2018 Shanghai 2300
three 2017 Guangzhou 1000
four 2018 Shenzhen 700
five 2017 Hanghzhou 500
six 2017 Chongqing 500 
# DataFrame可以视作Series的集合
apt

Beijing      55000.0
Shanghai     60000.0
Shenzhen     70000.0
Hangzhou     45000.0
Guangzhou    45000.0
Suzhou           NaN
Name: income, dtype: float64

cars

Beijing      350000
Shanghai     400000
Shenzhen     300000
Tianjing     200000
Guangzhou    250000
Chongqing    150000
dtype: int64

df = pd.DataFrame({'apt':apt,'cars':cars})

df
apt cars
Beijing 55000.0 350000.0
Chongqing NaN 150000.0
Guangzhou 45000.0 250000.0
Hangzhou 45000.0 NaN
Shanghai 60000.0 400000.0
Shenzhen 70000.0 300000.0
Suzhou NaN NaN
Tianjing NaN 200000.0 
# 取出一列
df['apt']

Beijing      55000.0
Chongqing        NaN
Guangzhou    45000.0
Hangzhou     45000.0
Shanghai     60000.0
Shenzhen     70000.0
Suzhou           NaN
Tianjing         NaN
Name: apt, dtype: float64

type(df['apt'])

pandas.core.series.Series

df[['apt']]
apt
Beijing 55000.0
Chongqing NaN
Guangzhou 45000.0
Hangzhou 45000.0
Shanghai 60000.0
Shenzhen 70000.0
Suzhou NaN
Tianjing NaN 
type(df[['apt']])

pandas.core.frame.DataFrame

# 赋值
df
apt cars
Beijing 55000.0 350000.0
Chongqing NaN 150000.0
Guangzhou 45000.0 250000.0
Hangzhou 45000.0 NaN
Shanghai 60000.0 400000.0
Shenzhen 70000.0 300000.0
Suzhou NaN NaN
Tianjing NaN 200000.0 
df['bonus'] = 40000

df
apt cars bonus
Beijing 55000.0 350000.0 40000
Chongqing NaN 150000.0 40000
Guangzhou 45000.0 250000.0 40000
Hangzhou 45000.0 NaN 40000
Shanghai 60000.0 400000.0 40000
Shenzhen 70000.0 300000.0 40000
Suzhou NaN NaN 40000
Tianjing NaN 200000.0 40000 
# 对两列做计算
df['expense'] = df['apt'] + df['bonus']

df
apt cars bonus expense
Beijing 55000.0 350000.0 40000 95000.0
Chongqing NaN 150000.0 40000 NaN
Guangzhou 45000.0 250000.0 40000 85000.0
Hangzhou 45000.0 NaN 40000 85000.0
Shanghai 60000.0 400000.0 40000 100000.0
Shenzhen 70000.0 300000.0 40000 110000.0
Suzhou NaN NaN 40000 NaN
Tianjing NaN 200000.0 40000 NaN 
df.index

Index(['Beijing', 'Chongqing', 'Guangzhou', 'Hangzhou', 'Shanghai', 'Shenzhen',
       'Suzhou', 'Tianjing'],
      dtype='object')

df.loc['Beijing']

apt         55000.0
cars       350000.0
bonus       40000.0
expense     95000.0
Name: Beijing, dtype: float64

type(df.loc['Beijing'])

pandas.core.series.Series

df.loc[['Beijing','Shanghai','Guangzhou']]
apt cars bonus expense
Beijing 55000.0 350000.0 40000 95000.0
Shanghai 60000.0 400000.0 40000 100000.0
Guangzhou 45000.0 250000.0 40000 85000.0 
df
apt cars bonus expense
Beijing 55000.0 350000.0 40000 95000.0
Chongqing NaN 150000.0 40000 NaN
Guangzhou 45000.0 250000.0 40000 85000.0
Hangzhou 45000.0 NaN 40000 85000.0
Shanghai 60000.0 400000.0 40000 100000.0
Shenzhen 70000.0 300000.0 40000 110000.0
Suzhou NaN NaN 40000 NaN
Tianjing NaN 200000.0 40000 NaN 
# loc高级用法
df.loc['Beijing':'Suzhou',['apt','bonus']]
apt bonus
Beijing 55000.0 40000
Chongqing NaN 40000
Guangzhou 45000.0 40000
Hangzhou 45000.0 40000
Shanghai 60000.0 40000
Shenzhen 70000.0 40000
Suzhou NaN 40000 
# 类似切片的用法
df.loc['Beijing':'Suzhou','apt':'bonus']
apt cars bonus
Beijing 55000.0 350000.0 40000
Chongqing NaN 150000.0 40000
Guangzhou 45000.0 250000.0 40000
Hangzhou 45000.0 NaN 40000
Shanghai 60000.0 400000.0 40000
Shenzhen 70000.0 300000.0 40000
Suzhou NaN NaN 40000 
# 传入list的用法
df.loc[['Beijing','Suzhou'],['apt','bonus']]
apt bonus
Beijing 55000.0 40000
Suzhou NaN 40000 
df.loc['Beijing','bonus'] = 50000

df
apt cars bonus expense
Beijing 55000.0 350000.0 50000 95000.0
Chongqing NaN 150000.0 40000 NaN
Guangzhou 45000.0 250000.0 40000 85000.0
Hangzhou 45000.0 NaN 40000 85000.0
Shanghai 60000.0 400000.0 40000 100000.0
Shenzhen 70000.0 300000.0 40000 110000.0
Suzhou NaN NaN 40000 NaN
Tianjing NaN 200000.0 40000 NaN 
# 对一列赋值
df.loc[:,'expense'] = 100000

df
apt cars bonus expense
Beijing 55000.0 350000.0 50000 100000
Chongqing NaN 150000.0 40000 100000
Guangzhou 45000.0 250000.0 40000 100000
Hangzhou 45000.0 NaN 40000 100000
Shanghai 60000.0 400000.0 40000 100000
Shenzhen 70000.0 300000.0 40000 100000
Suzhou NaN NaN 40000 100000
Tianjing NaN 200000.0 40000 100000 
df.shape

(8, 4)

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, Beijing to Tianjing
Data columns (total 4 columns):
apt        5 non-null float64
cars       6 non-null float64
bonus      8 non-null int64
expense    8 non-null int64
dtypes: float64(2), int64(2)
memory usage: 640.0+ bytes

df.T
Beijing Chongqing Guangzhou Hangzhou Shanghai Shenzhen Suzhou Tianjing
apt 55000.0 NaN 45000.0 45000.0 60000.0 70000.0 NaN NaN
cars 350000.0 150000.0 250000.0 NaN 400000.0 300000.0 NaN 200000.0
bonus 50000.0 40000.0 40000.0 40000.0 40000.0 40000.0 40000.0 40000.0
expense 100000.0 100000.0 100000.0 100000.0 100000.0 100000.0 100000.0 100000.0 
df
apt cars bonus expense
Beijing 55000.0 350000.0 50000 100000
Chongqing NaN 150000.0 40000 100000
Guangzhou 45000.0 250000.0 40000 100000
Hangzhou 45000.0 NaN 40000 100000
Shanghai 60000.0 400000.0 40000 100000
Shenzhen 70000.0 300000.0 40000 100000
Suzhou NaN NaN 40000 100000
Tianjing NaN 200000.0 40000 100000 
df.describe()
apt cars bonus expense
count 5.000000 6.000000 8.000000 8.0
mean 55000.000000 275000.000000 41250.000000 100000.0
std 10606.601718 93541.434669 3535.533906 0.0
min 45000.000000 150000.000000 40000.000000 100000.0
25% 45000.000000 212500.000000 40000.000000 100000.0
50% 55000.000000 275000.000000 40000.000000 100000.0
75% 60000.000000 337500.000000 40000.000000 100000.0
max 70000.000000 400000.000000 50000.000000 100000.0 
df['cars']

Beijing      350000.0
Chongqing    150000.0
Guangzhou    250000.0
Hangzhou          NaN
Shanghai     400000.0
Shenzhen     300000.0
Suzhou            NaN
Tianjing     200000.0
Name: cars, dtype: float64

df['cars'] < 310000

Beijing      False
Chongqing     True
Guangzhou     True
Hangzhou     False
Shanghai     False
Shenzhen      True
Suzhou       False
Tianjing      True
Name: cars, dtype: bool

df.loc[:,'color'] = ['红','黄','紫','蓝','红','绿','棕','红']
df
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Chongqing NaN 150000.0 40000 100000 黄
Guangzhou 45000.0 250000.0 40000 100000 紫
Hangzhou 45000.0 NaN 40000 100000 蓝
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Suzhou NaN NaN 40000 100000 棕
Tianjing NaN 200000.0 40000 100000 红 
df['color'].isin(['红','绿'])

Beijing       True
Chongqing    False
Guangzhou    False
Hangzhou     False
Shanghai      True
Shenzhen      True
Suzhou       False
Tianjing      True
Name: color, dtype: bool

df[df['color'].isin(['红','绿'])]
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Tianjing NaN 200000.0 40000 100000 红 
# 填充缺失值
df.fillna(value=50000)
#df.fillna(value=50000, inplace=True) 就地填充，改变原始值
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Chongqing 50000.0 150000.0 40000 100000 黄
Guangzhou 45000.0 250000.0 40000 100000 紫
Hangzhou 45000.0 50000.0 40000 100000 蓝
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Suzhou 50000.0 50000.0 40000 100000 棕
Tianjing 50000.0 200000.0 40000 100000 红 
df
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Chongqing NaN 150000.0 40000 100000 黄
Guangzhou 45000.0 250000.0 40000 100000 紫
Hangzhou 45000.0 NaN 40000 100000 蓝
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Suzhou NaN NaN 40000 100000 棕
Tianjing NaN 200000.0 40000 100000 红 
df.fillna(method='ffill') #缺失值使用前一行的数据填充
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Chongqing 55000.0 150000.0 40000 100000 黄
Guangzhou 45000.0 250000.0 40000 100000 紫
Hangzhou 45000.0 250000.0 40000 100000 蓝
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Suzhou 70000.0 300000.0 40000 100000 棕
Tianjing 70000.0 200000.0 40000 100000 红 
# 向后填充
df.fillna(method='bfill')
apt cars bonus expense color
Beijing 55000.0 350000.0 50000 100000 红
Chongqing 45000.0 150000.0 40000 100000 黄
Guangzhou 45000.0 250000.0 40000 100000 紫
Hangzhou 45000.0 400000.0 40000 100000 蓝
Shanghai 60000.0 400000.0 40000 100000 红
Shenzhen 70000.0 300000.0 40000 100000 绿
Suzhou NaN 200000.0 40000 100000 棕
Tianjing NaN 200000.0 40000 100000 红

从外部读入文件

stock = pd.read_csv('stock_px.csv')

stock.head() #显示前5行
DATE AA AAPL GE IBM JNJ MSFT PEP SPX XOM
0 1990-2-1 0:00 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.12
1 1990-2-2 0:00 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.24
2 1990-2-5 0:00 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.25
3 1990-2-6 0:00 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.23
4 1990-2-7 0:00 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33 
#将第一列（时间）作为索引
stock = pd.read_csv('stock_px.csv', index_col=0, parse_dates=['DATE']) #解析成日期型，作用见后续

stock.head(6)
AA AAPL GE IBM JNJ MSFT PEP SPX XOM
DATE
1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.12
1990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.24
1990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.25
1990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.23
1990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33
1990-02-08 5.04 7.71 2.92 17.86 4.46 0.51 6.22 332.96 6.35 
stock.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 5472 entries, 1990-02-01 to 2011-10-14
Data columns (total 9 columns):
AA      5472 non-null float64
AAPL    5472 non-null float64
GE      5472 non-null float64
IBM     5472 non-null float64
JNJ     5472 non-null float64
MSFT    5472 non-null float64
PEP     5471 non-null float64
SPX     5472 non-null float64
XOM     5472 non-null float64
dtypes: float64(9)
memory usage: 427.5 KB

stock.describe()
AA AAPL GE IBM JNJ MSFT PEP SPX XOM
count 5472.000000 5472.000000 5472.000000 5472.000000 5472.000000 5472.000000 5471.000000 5472.000000 5472.000000
mean 17.440285 57.119313 17.933194 66.637730 34.225340 17.046345 34.284204 945.035216 35.225919
std 9.647999 88.670423 10.647635 41.689481 19.726666 11.000988 18.383894 369.494672 23.967647
min 4.200000 3.230000 2.400000 8.400000 4.200000 0.510000 5.870000 295.460000 5.940000
25% 8.077500 8.760000 6.280000 20.577500 12.160000 4.240000 15.480000 547.217500 11.910000
50% 14.885000 11.990000 18.150000 74.115000 37.570000 20.910000 33.750000 1058.305000 30.280000
75% 26.340000 68.017500 27.300000 95.657500 52.675000 25.310000 49.530000 1253.395000 55.300000
max 43.620000 422.000000 42.780000 190.530000 67.320000 46.810000 71.250000 1565.150000 87.480000 
stock.tail()  #默认打印后5行
AA AAPL GE IBM JNJ MSFT PEP SPX XOM
DATE
2011-10-10 10.09 388.81 16.14 186.62 64.43 26.94 61.87 1194.89 76.28
2011-10-11 10.30 400.29 16.14 185.00 63.96 27.00 60.95 1195.54 76.27
2011-10-12 10.05 402.19 16.40 186.12 64.33 26.96 62.70 1207.25 77.16
2011-10-13 10.10 408.43 16.22 186.82 64.23 27.18 62.36 1203.66 76.37
2011-10-14 10.26 422.00 16.60 190.53 64.72 27.27 62.24 1224.58 78.11 
stock.index

DatetimeIndex(['1990-02-01', '1990-02-02', '1990-02-05', '1990-02-06',
               '1990-02-07', '1990-02-08', '1990-02-09', '1990-02-12',
               '1990-02-13', '1990-02-14',
               ...
               '2011-10-03', '2011-10-04', '2011-10-05', '2011-10-06',
               '2011-10-07', '2011-10-10', '2011-10-11', '2011-10-12',
               '2011-10-13', '2011-10-14'],
              dtype='datetime64[ns]', name='DATE', length=5472, freq=None)
  • 解析为日期型的作用
stock.loc[:,'dow'] = stock.index.dayofweek  #添加一个名为'dow'的新列，其内容为该日为星期几

stock.head()
AA AAPL GE IBM JNJ MSFT PEP SPX XOM dow
DATE
1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.12 3
1990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.24 4
1990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.25 0
1990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.23 1
1990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33 2 
stock.loc[:,'doy'] = stock.index.dayofyear  #添加一个名为'doy'的新列，其内容为一年当中的第几天

stock.head()
AA AAPL GE IBM JNJ MSFT PEP SPX XOM dow doy
DATE
1990-02-01 4.98 7.86 2.87 16.79 4.27 0.51 6.04 328.79 6.12 3 32
1990-02-02 5.04 8.00 2.87 16.89 4.37 0.51 6.09 330.92 6.24 4 33
1990-02-05 5.07 8.18 2.87 17.32 4.34 0.51 6.05 331.85 6.25 0 36
1990-02-06 5.01 8.12 2.88 17.56 4.32 0.51 6.15 329.66 6.23 1 37
1990-02-07 5.04 7.77 2.91 17.93 4.38 0.51 6.17 333.75 6.33 2 38
  • 作图
%matplotlib inline
stock['JNJ'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x19bfb184788>

在这里插入图片描述

stock['JNJ'].plot(grid=True)  #网格线

<matplotlib.axes._subplots.AxesSubplot at 0x19bfc4b9188>

在这里插入图片描述

  • 条件与或非

    | 或

    & 且

    ! 非 
sto = stock[(stock.index >= '2009-01-01') & (stock.index <= '2009-1-31')] #2009年1月份的数据

sto['JNJ'].plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x19bfc7eaa48>

在这里插入图片描述

stock[(stock.index >= '2009-01-01') & (stock.index < '2009-1-31')].describe()
AA AAPL GE IBM JNJ MSFT PEP SPX XOM dow doy
count 20.000000 20.000000 20.000000 20.00000 20.00000 20.000000 20.000000 20.000000 20.000000 20.00000 20.000000
mean 9.187000 88.775000 12.947500 83.65950 52.89300 17.768000 47.889500 865.575500 73.170500 2.20000 16.650000
std 1.364208 4.377831 1.532359 3.34427 1.16083 1.135261 1.694649 38.874233 1.590811 1.43637 8.851256
min 7.450000 78.200000 10.860000 77.87000 50.94000 16.060000 46.150000 805.220000 70.230000 0.00000 2.000000
25% 8.057500 87.115000 11.747500 80.96000 52.15000 16.582500 46.490000 839.322500 71.930000 1.00000 8.750000
50% 8.805000 89.885000 12.525000 83.19000 52.89000 18.140000 47.345000 847.915000 73.100000 2.00000 15.500000
75% 10.360000 91.432500 14.475000 86.87250 53.71750 18.545000 48.457500 894.425000 73.997500 3.25000 23.750000
max 11.600000 94.580000 15.420000 90.06000 55.20000 19.500000 51.420000 934.700000 76.350000 4.00000 30.000000
  • 写入文件
sto.to_csv('sto.csv')

sto.to_csv('n_sto.csv', index=False) #不加索引

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