python数据分析-09pandas绘图

Posted 2022-03-14 nikecode

#38
#Pandas绘图之Series
import pandas as pd
import numpy as np
from pandas import Series,DataFrame
import matplotlib.pyplot as plt

"""
#cumsum()函数求和
s = Series([1,2,3,4,5])
print(s)
# 0    1
# 1    2
# 2    3
# 3    4
# 4    5
# dtype: int64
print(s.cumsum())#递加求和
# 0     1
# 1     3
# 2     6
# 3    10
# 4    15
# dtype: int64

s1 = Series(np.random.randn(1000)).cumsum()
s2 = Series(np.random.randn(1000)).cumsum()
# s1.plot(kind="line",grid=True,label="S1",title="This is Series",style="--")
# s2.plot(label="S2")
# plt.legend()#将label图例显示出来
# plt.show()

#画子图:方法1
# fig,ax = plt.subplots(2,1)#两行一列的子图，ax是画笔
# ax[0].plot(s1)
# ax[1].plot(s2)
# plt.show()

#画子图:方法2
# fig,ax = plt.subplots(2,1)#两行一列的子图，ax是画笔
# s1.plot(ax=ax[0],label="S1")
# s2.plot(ax=ax[1],label="S2")
# plt.show()

#设置不同图像款式
fig,ax = plt.subplots(2,1)#两行一列的子图，ax是画笔
s1[:10].plot(ax=ax[0],label="S1",kind="bar")
s2.plot(ax=ax[1],label="S2")
plt.show()
"""

"""
#Pandas绘图之DataFrame
df = DataFrame(
    np.random.randint(1,10,40).reshape(10,4),
    columns=["A","B","C","D"]
)
#print(df)
#    A  B  C  D
# 0  9  5  4  9
# 1  1  9  7  3
# 2  1  1  2  6
# 3  1  8  4  9
# 4  1  8  8  4
# 5  8  9  9  2
# 6  1  7  1  6
# 7  5  6  8  7
# 8  8  6  4  6
# 9  8  1  4  6
#df.plot()#默认按照列画图
#df.plot(kind="bar")
#df.plot(kind="barh")
#df.plot(kind="bar",stacked=True)#叠加条形图
# df.plot(kind="area")#填充线形图
# plt.show()
#
# print(df.iloc[5])
# # A    3
# # B    8
# # C    9
# # D    2
# # Name: 5, dtype: int32
# df.iloc[5].plot()
# plt.show()

#按照行去画图：
# for i in df.index:
#     df.iloc[i].plot(label=str(i))
# plt.legend()
# plt.show()

#按列画图：
# df["A"].plot()
# plt.show()

#按照行画图简单用法
# df.T.plot()
# plt.show()
"""


#------------
#matplotlib的直方图和密度图
#直方图：
s = Series(np.random.randn(1000))
# plt.hist(s)
# plt.show()

# re = plt.hist(s,rwidth=0.9)
# print(type(re))#<class ‘tuple‘>
# print(len(re))#3
# print(re[0])#[  8.  44. 107. 178. 245. 198. 135.  61.  19.   5.]#代表频率，出现的次数
# print(re[1])#[-3.41543154 -2.7680023  -2.12057305 -1.47314381 -0.82571456 -0.17828532 0.46914393  1.11657317  1.76400242  2.41143166  3.05886091]
# #表示取值的间隔
# print(re[2])#<a list of 10 Patch objects>#表示有10个矩形

#设置相关参数
# plt.hist(s,rwidth=0.9,bins=20,color="r")
# plt.show()


#密度图画法：
# s.plot()
# plt.show()
#kde画密度图：
s.plot(kind=‘kde‘)
plt.show()