-
pandas是一个强大的Python数据分析的工具包，是基于NumPy构建
-
pandas的主要功能：
- 具备对其功能的数据结构DataFrame、Series
- 集成时间序列功能
- 提供丰富的教学运算和 *** 作
- 灵活处理缺失数据
-
安装：pip3 install pandas
1、简介
Series是一种类似于一维数组的对象，由一组数据和一组与之相关的数据标签（索引）组成
Series比较像列表（数组）和字典的结合体
Series支持array的特性（下标）：
从ndarray创建Series：Series(arr)
与标量运算：sr*2
两个Series运算：sr1+sr2
索引：sr[0],sr[[1, 2, 3]]
切片：sr[0:2]
通用函数：np.abs(sr)
布尔值过滤：sr[sr>0]
Series支持字典的特性（标签）：
从字典创建Series：Series(dic)
in运算：'a' in sr
键索引：sr['a'],sr[['a', 'b', 'd']]
import pandas as pd
import numpy as np
print(pd.Series([2, 3, 4]))
print('-------------------')
print(pd.Series([2, 3, 4], index=['a', 'b', 'c']))
print('-------------------')
print(pd.Series(np.arange(3)))
结果：
0 2
1 3
2 4
dtype: int64
-------------------
a 2
b 3
c 4
dtype: int64
-------------------
0 0
1 1
2 2
dtype: int64
import pandas as pd
import numpy as np
sr = pd.Series(np.arange(4))
sr1 = sr[2:].copy()
print(sr1)
print('-----------------------')
print(sr1.loc[3], sr1.iloc[0])
结果：
2 2
3 3
dtype: int64
-----------------------
3 2
import pandas as pd
sr1 = pd.Series([1, 2, 3], index=['c', 'a', 'b'])
sr2 = pd.Series([4, 5, 6], index=['b', 'c', 'a'])
sr3 = pd.Series([4, 5, 6, 7], index=['b', 'c', 'a', 'd'])
print(sr1 + sr2)
print('------------')
print(sr1 + sr3)
print('------------')
print(sr1.add(sr3, fill_value=0))
结果：
a 8
b 7
c 6
dtype: int64
------------
a 8.0
b 7.0
c 6.0
d NaN
dtype: float64
------------
a 8.0
b 7.0
c 6.0
d 7.0
dtype: float64
import pandas as pd
sr1 = pd.Series([1, 2, 3], index=['c', 'a', 'b'])
sr2 = pd.Series([4, 5, 6], index=['b', 'c', 'd'])
sr = sr1 + sr2
print(sr)
print('-------------------')
print(sr.isnull())
print('-------------------')
print(sr.notnull())
print('-------处理缺失值-------')
print(sr[sr.notnull()])
print('-------处理缺失值-------')
print(sr.dropna())
print('-------------------')
print(sr.fillna(0))
print('-------------------')
print(sr.fillna(sr.mean()))
结果：
a NaN
b 7.0
c 6.0
d NaN
dtype: float64
-------------------
a True
b False
c False
d True
dtype: bool
-------------------
a False
b True
c True
d False
dtype: bool
-------处理缺失值-------
b 7.0
c 6.0
dtype: float64
-------处理缺失值-------
b 7.0
c 6.0
dtype: float64
-------------------
a 0.0
b 7.0
c 6.0
d 0.0
dtype: float64
-------------------
a 6.5
b 7.0
c 6.0
d 6.5
dtype: float64
1、DataFrame创建DataFrame是一个表格型的数据结构，含有一组有序的列。DataFrame可以被看做是由Series组成的字典
import pandas as pd
df = pd.DataFrame({'one': [1, 2, 3], 'tow': [4, 5, 6]}, index=['a', 'b', 'c'])
df1 = pd.DataFrame(
{'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two': pd.Series([1, 2, 3, 4], index=['b', 'a', 'c', 'd'])})
print(df)
print('--------------')
print(df1)
df1.to_csv('df1.csv')
print('--------------')
print(pd.read_csv('test.csv'))
结果：
one tow
a 1 4
b 2 5
c 3 6
--------------
one two
a 1.0 2
b 2.0 1
c 3.0 3
d NaN 4
--------------
a b c
0 1 2 3
1 4 5 6
2 7 8 9
index 获取索引
T 转置
columns 获取列索引
values 获取值数组
describe() 获取快速统计
import pandas as pd
df = pd.DataFrame({'one': [1, 2, 3], 'tow': [4, 5, 6]}, index=['a', 'b', 'c'])
print(df)
print('---------------')
print(df.index)
print('---------------')
print(df.values)
print('---------------')
print(df.T)
print('---------------')
print(df.columns)
print('---------------')
print(df.describe())
结果：
one tow
a 1 4
b 2 5
c 3 6
---------------
Index(['a', 'b', 'c'], dtype='object')
---------------
[[1 4]
[2 5]
[3 6]]
---------------
a b c
one 1 2 3
tow 4 5 6
---------------
Index(['one', 'tow'], dtype='object')
---------------
one tow
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
25% 1.5 4.5
50% 2.0 5.0
75% 2.5 5.5
max 3.0 6.0
- DataFrame是一个二维数组类型，所以有行索引和列索引
- DataFrame同样可以通过标签和位置两种方法进行索引和切片
- loc属性和iloc属性
- 使用方法：逗号隔开，前面是行索引，后面是列索引
- 行/列索引部分可以是常规索引、切片、布尔值索引任意搭配
import pandas as pd
df = pd.DataFrame({'one': [1, 2, 3], 'two': [4, 5, 6]}, index=['a', 'b', 'c'])
print(df)
print('---------------')
print(df.loc['b', 'one'])
print('---------------')
print(df.loc['a', :])
结果：
one two
a 1 4
b 2 5
c 3 6
---------------
2
one 1
tow 4
Name: a, dtype: int64
- DataFrame对象在运算时，同样会进行数据对齐，其行索引和列索引分别对齐
- DataFrame处理缺失数据的相关的方法：
- dropna(axis=0,where=‘any’,…)
- fillna()
- isnull()
- notnull()
import pandas as pd
import numpy as np
df = pd.DataFrame({'one': [1, 2, 3], 'two': [4, 5, 6]}, index=['a', 'b', 'c'])
df1 = pd.DataFrame({'one': [1, 2, 3, 4], 'two': [5, 6, 7, 8]}, index=['a', 'b', 'c', 'd'])
df.loc['c', 'two'] = np.nan
df2 = df + df1
print(df2)
print('-----------------')
print(df2.fillna(0))
print('-----------------')
print(df2.dropna())
print('-----------------')
print(df2.dropna(how='all'))
print('-----------------')
print(df2.dropna(how='any'))
print('-----------------')
print(df2.loc['c', 'one'])
print('-----------------')
print(df)
print(df.dropna(axis=0)) # 行
print(df.dropna(axis=1)) # 列
结果：
one two
a 2.0 9.0
b 4.0 11.0
c 6.0 NaN
d NaN NaN
-----------------
one two
a 2.0 9.0
b 4.0 11.0
c 6.0 0.0
d 0.0 0.0
-----------------
one two
a 2.0 9.0
b 4.0 11.0
-----------------
one two
a 2.0 9.0
b 4.0 11.0
c 6.0 NaN
-----------------
one two
a 2.0 9.0
b 4.0 11.0
-----------------
6.0
-----------------
one two
a 1 4.0
b 2 5.0
c 3 NaN
one
a 1
b 2
c 3
one two
a 1 4.0
b 2 5.0
mean(axis=0,skipna=Faluse) 对列(行)求平均值
sum(axis=1) 对列(行)求和
sort_index(axis, ..., ascending) 对列(行)索引排序
sort_values(by, axis, ascending) 按某一列(行)的值排序
import pandas as pd
import numpy as np
df = pd.DataFrame({'one': [2, 1, 3], 'two': [5, 4, 6]}, index=['a', 'b', 'c'])
df.loc['c', 'two'] = np.nan
print(df)
print('--------------------')
print(df.mean())
print('--------------------')
print(df.mean(axis=1))
print('--------------------')
print(df.sum(axis=1))
print('--------------------')
print(df.sort_values(by='one', ascending=False))
print('--------------------')
print(df.sort_index(ascending=False, axis=1))
结果：
one two
a 2 5.0
b 1 4.0
c 3 NaN
--------------------
one 2.0
two 4.5
dtype: float64
--------------------
a 3.5
b 2.5
c 3.0
dtype: float64
--------------------
a 7.0
b 5.0
c 3.0
dtype: float64
--------------------
one two
c 3 NaN
a 2 5.0
b 1 4.0
--------------------
two one
a 5.0 2
b 4.0 1
c NaN 3
1、时间处理对象
产生时间对象数组：date_range
start 开始时间
end 结束时间
periods 时间长度
freq 时间频率，默认为'D'，可以H(our),W(eek),B(usiness),S(emi-)M(onth),(min)T(es),S(encond),A(year),...
import pandas as pd
import datetime, dateutil
x = dateutil.parser.parse('02/03/2001')
print(x, type(x))
print(pd.date_range('2022-1-1', '2022-2-1'))
print(pd.date_range('2022-1-1', periods=10, freq='H'))
结果：
2001-02-03 00:00:00
DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
'2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',
'2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',
'2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',
'2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20',
'2022-01-21', '2022-01-22', '2022-01-23', '2022-01-24',
'2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',
'2022-01-29', '2022-01-30', '2022-01-31', '2022-02-01'],
dtype='datetime64[ns]', freq='D')
DatetimeIndex(['2022-01-01 00:00:00', '2022-01-01 01:00:00',
'2022-01-01 02:00:00', '2022-01-01 03:00:00',
'2022-01-01 04:00:00', '2022-01-01 05:00:00',
'2022-01-01 06:00:00', '2022-01-01 07:00:00',
'2022-01-01 08:00:00', '2022-01-01 09:00:00'],
dtype='datetime64[ns]', freq='H')
import numpy as np
import pandas as pd
sr = pd.Series(np.arange(50), index=pd.date_range('2021-12-25', periods=50))
print(sr)
print('-----------------------------')
print(sr['2022-02'])
print('-----------------------------')
print(sr['2021'])
print('-----------------------------')
print(sr['2021-12-25':'2021-12-27'])
print('-----------------------------')
print(sr.resample('W').sum()) # 周求和,月：M
结果：
2021-12-25 0
2021-12-26 1
2021-12-27 2
2021-12-28 3
2021-12-29 4
2021-12-30 5
2021-12-31 6
2022-01-01 7
2022-01-02 8
2022-01-03 9
2022-01-04 10
2022-01-05 11
2022-01-06 12
2022-01-07 13
2022-01-08 14
2022-01-09 15
2022-01-10 16
2022-01-11 17
2022-01-12 18
2022-01-13 19
2022-01-14 20
2022-01-15 21
2022-01-16 22
2022-01-17 23
2022-01-18 24
2022-01-19 25
2022-01-20 26
2022-01-21 27
2022-01-22 28
2022-01-23 29
2022-01-24 30
2022-01-25 31
2022-01-26 32
2022-01-27 33
2022-01-28 34
2022-01-29 35
2022-01-30 36
2022-01-31 37
2022-02-01 38
2022-02-02 39
2022-02-03 40
2022-02-04 41
2022-02-05 42
2022-02-06 43
2022-02-07 44
2022-02-08 45
2022-02-09 46
2022-02-10 47
2022-02-11 48
2022-02-12 49
Freq: D, dtype: int64
-----------------------------
2022-02-01 38
2022-02-02 39
2022-02-03 40
2022-02-04 41
2022-02-05 42
2022-02-06 43
2022-02-07 44
2022-02-08 45
2022-02-09 46
2022-02-10 47
2022-02-11 48
2022-02-12 49
Freq: D, dtype: int64
-----------------------------
2021-12-25 0
2021-12-26 1
2021-12-27 2
2021-12-28 3
2021-12-29 4
2021-12-30 5
2021-12-31 6
Freq: D, dtype: int64
-----------------------------
2021-12-25 0
2021-12-26 1
2021-12-27 2
Freq: D, dtype: int64
-----------------------------
2021-12-26 1
2022-01-02 35
2022-01-09 84
2022-01-16 133
2022-01-23 182
2022-01-30 231
2022-02-06 280
2022-02-13 279
Freq: W-SUN, dtype: int64
1、简介
- 数据文件常用格式：csv
- pandas读取文件：从文件名、URL、文件对象中加载数据
- read_csv：默认分隔符为逗号
- read_table：默认分隔符为制表符
read_csv、read_table函数主要参数：
sep 指定分隔符，可用正则表达式入'\s+'
header=None 指定文件无列名
name 指定列名
index_col 指定某列作为索引
skip_row 指定跳过某些行
na_values 指定某些字符串表示缺失值
parse_dates 指定某些列是否被解析为日期，类型为布尔值或列表
import pandas as pd
# parse_dates:解析为时间对象,默认为str
df = pd.read_csv('601318.csv', index_col='date', parse_dates=True)
print(df)
df = pd.read_csv('601318.csv', header=None, names=list('abcdefg'))
print(df)
结果：
Unnamed: 0 open close high low volume code
date
2020-04-03 0 69.10 68.86 69.26 68.41 42025417 601318
2020-04-02 1 68.40 69.67 69.67 67.76 51202929 601318
2020-04-01 2 69.00 69.32 70.47 68.90 55692869 601318
2020-03-31 3 70.11 69.17 70.35 69.01 42536786 601318
2020-03-30 4 68.60 69.15 69.39 68.45 46795596 601318
... ... ... ... ... ... ... ...
2019-01-11 297 58.00 58.07 58.29 57.50 45756973 601318
2019-01-10 298 56.87 57.50 57.82 56.55 67328223 601318
2019-01-09 299 56.20 56.95 57.60 55.96 81914613 601318
2019-01-08 300 56.05 55.80 56.09 55.20 55992092 601318
2019-01-07 301 57.09 56.30 57.17 55.90 76593007 601318
[302 rows x 7 columns]
a b c d e f g
NaN date open close high low volume code
0.0 2020/4/3 69.1 68.86 69.26 68.41 42025417 601318
1.0 2020/4/2 68.4 69.67 69.67 67.76 51202929 601318
2.0 2020/4/1 69 69.32 70.47 68.9 55692869 601318
3.0 2020/3/31 70.11 69.17 70.35 69.01 42536786 601318
... ... ... ... ... ... ... ...
297.0 2019/1/11 58 58.07 58.29 57.5 45756973 601318
298.0 2019/1/10 56.87 57.5 57.82 56.55 67328223 601318
299.0 2019/1/9 56.2 56.95 57.6 55.96 81914613 601318
300.0 2019/1/8 56.05 55.8 56.09 55.2 55992092 601318
301.0 2019/1/7 57.09 56.3 57.17 55.9 76593007 601318
主要参数：
sep 指定文件分隔符
na_rep 指定缺失值转换的字符串，默认为空字符串
header=False 不输出列名一行
index=False 不输出行索引一列
cols 指定输出的列，传入列表
1、简介
- Matplotlib是一个强大的Python绘图和数据可视化的工具包
- 安装方法：pip install matplotlib
plot函数：绘制折线图
线型linestyle(-,-.,--,..)
点型marker(v,^,s,*,H,+,x,D,o,...)
颜色color(b,g,r,y,k,w,...)
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [2, 8, 6, 10], "o-.", color='red') # 折线图
plt.show()
结果：
图像标注：
设置图像标题：plt.title() 设置y轴范围：plt.ylim()
设置x轴名称：plt.xlabel() 设置x轴刻度：plt.xticks()
设置y轴名称：plt.ylabel() 设置y轴刻度：plt.yticks()
设置x轴范围：plt.xlim() 设置曲线图例：plt.legend()
import matplotlib.pyplot as plt
import numpy as np
plt.plot([1, 2, 3, 4], [2, 8, 6, 10], "o-.", color='red', label='Line A') # 折线图
plt.plot([1, 2, 3, 4], [10, 7, 9, 6], color='green', marker='o', label='Line B')
plt.title('test Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.xticks(np.arange(0, 10, 2), ['a', 'b', 'c', 'd', 'e'])
plt.legend()
plt.show()
结果：
（1）画股票图像使用上面的csv文件
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('601318.csv',parse_dates=['date'], index_col='date')[['open','close','high','low']]
df.plot()
plt.show()
结果：
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1000, 1000, 10000)
y1 = x
y2 = x * x
y3 = 3 * x ** 3 + 5 * x ** 2 + 2 * x + 1
plt.plot(x, y1, color='red', label='y=x')
plt.plot(x, y2, color='green', label='y=x^x')
plt.plot(x, y3, color='black', label='3x^3+5x^2+2x+1')
plt.xlim(-1000, 1000)
plt.ylim(-1000, 1000)
plt.legend()
plt.show()
结果：
画布：figure
fig = plt.figure()
图：subplot
ax1 = fig.add_subplot(2,2,1)
调节子图间距：
subplots_adjust(left, bottom, right, top, wspace, hspace)
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1) # 两行两列,占第一个位置
ax1.plot([1, 2, 3, 4], [2, 4, 6, 8])
ax2 = fig.add_subplot(2, 2, 4)
ax2.plot([1, 2, 3, 4], [6, 8, 4, 7])
plt.show()
结果：
6、Matplotlib柱状图和饼图plt.plot(x,y,fmt,...) 坐标图
plt.boxplot(data,notch,position) 箱型图
plt.bar(left,height,width,bottom) 条形图
plt.barh(width,bottom,left,height) 横向条形图
plt.polar(theta, r) 极坐标图
plt.pie(data, explode) 饼图
plt.psd(x,NFFT=256,pad_to,Fs) 功率谱密度图
plt.specgram(x,NFFT=256,pad_to,F) 谱图
plt.cohere(x,y,NFFT=256,Fs) X-Y相关性函数
plt.scatter(x,y) 散点图
plt.step(x,y,where) 步阶图
plt.hist(x,bins,normed) 直方图
import matplotlib.pyplot as plt
import numpy as np
data = [32, 21, 36, 68]
label = ['Jan', 'Feb', 'Mar', 'Apr']
plt.bar(np.arange(len(data)), data, color=['green', 'red', 'black', 'yellow'], width=0.3, align='edge')
plt.xticks(np.arange(len(data)), labels=label)
# plt.bar([1, 2, 3, 4], [6, 8, 4, 7])
plt.show()
结果：
import matplotlib.pyplot as plt
plt.pie([10, 20, 30, 40], labels=['a', 'b', 'c', 'd'], autopct="%.2f%%", explode=[0, 0, 0, 0.1])
plt.show()
结果：
7、Matplotlib绘制K线图安装：pip3 install mplfinance
import matplotlib.pyplot as plt
import pandas as pd
import mplfinance as mpf
from matplotlib.dates import date2num
df = pd.read_csv('601318.csv', index_col='date', parse_dates=True)
df['time'] = date2num(df.index.to_pydatetime())
print(df)
mycolor = mpf.make_marketcolors(up="red", down="green", edge="i", wick="i", volume="in")
mystyle = mpf.make_mpf_style(marketcolors=mycolor, gridaxis="both", grid)
mpf.plot(df, type="candle", mav=(5, 10, 20), style=mystyle, volume=True, show_nontrading=False)
plt.show()
结果：
Unnamed: 0 open close high low volume code time
date
2020-04-03 0 69.10 68.86 69.26 68.41 42025417 601318 18355.0
2020-04-02 1 68.40 69.67 69.67 67.76 51202929 601318 18354.0
2020-04-01 2 69.00 69.32 70.47 68.90 55692869 601318 18353.0
2020-03-31 3 70.11 69.17 70.35 69.01 42536786 601318 18352.0
2020-03-30 4 68.60 69.15 69.39 68.45 46795596 601318 18351.0
... ... ... ... ... ... ... ... ...
2019-01-11 297 58.00 58.07 58.29 57.50 45756973 601318 17907.0
2019-01-10 298 56.87 57.50 57.82 56.55 67328223 601318 17906.0
2019-01-09 299 56.20 56.95 57.60 55.96 81914613 601318 17905.0
2019-01-08 300 56.05 55.80 56.09 55.20 55992092 601318 17904.0
2019-01-07 301 57.09 56.30 57.17 55.90 76593007 601318 17903.0
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