1. 列表测试
num_list=[1,2,3,4,5,6]
print('num_list:{}'.format(num_list))
print('num_list[-1]:{}'.format(num_list[-1]))
print('num_list[-2]:{}'.format(num_list[-2]))
print('num_list[:3]:{}'.format(num_list[:3]))
print('num_list[:3:]:{}'.format(num_list[:3:]))
print('num_list[0:3:]:{}'.format(num_list[0:3:]))
print('num_list[3:]:{}'.format(num_list[3:]))
print('num_list[3::]:{}'.format(num_list[3::]))
print('num_list[::2]:{}'.format(num_list[::2]))
print('num_list[0::2]:{}'.format(num_list[0::2]))
2.时间测试
import time
import datetime
ts=time.time()
date_time=time.localtime(ts)
print(date_time)
print(time.strftime("当前时间：%Y-%m-%d %H:%M:%S"))
print(time.strftime("当前时间：%Y-%m-%d %H:%M:%S",time.localtime(1514829722)))
time.sleep(10)
print(time.mktime(time.localtime()))
current_time=datetime.datetime.now()
print("默认格式：{}".format(current_time))
3. json 测试
import json
user_info_dict={"aa":"sz",
"ag":1,
"lag":["p","y"],
"if_vip":True,
"gender":None}
json_str=json.dumps(user_info_dict)
python_dict=json.loads(json_str)
print(json_str)
print(type(json_str))
print(type(user_info_dict))
print(type(python_dict))
4.正则测试
import re
str="p j p c"
rs=re.match("p",str)
print (rs)
print (rs.group())
rs=re.match(".","1")
print (rs.group())
rs=re.match("\s","\t")
print (rs)
rs=re.match("[ab]","bcc")
print (rs.group())
str=r"adb\\abc"
print (str)
html_data="<head><title>python</title></head>"
rs=re.match(r"<(.+)><(.+)>.+</\2></\1>",html_data)
print(rs)
rs=re.match(r"<(?P<g1>.+)><(?P<g2>.+)>.+</(?P=g2)></(?P=g1)>",html_data)
print(rs)
patten=re.compile("\w{4,10}@163\.com")
rs=re.match(patten,"python2018@163.com,python2020@163.com")
print(rs)
rs=re.match(patten,"abc@123.com")
print(rs)
rs=re.search(patten,"python2018@163.com,python2020@163.com")
print(rs)
rs=re.findall(patten,"python2018@163.com,python2020@163.com")
print(rs)
patten=re.compile("(\w{4,10}@(163|qq)\.com)")
rs=re.findall(patten,"python2018@163.com,python2020@qq.com")
print(rs)
5. url访问测试
import urllib
response=urllib.urlopen("http://www.baidu.com")
print (response.read().decode("utf8"))
6. numpy测试
import numpy as py
a=[1,1,2,2,3,3,4,4]
arr=np.array(a)
print('a:{}'.format(a))
print ('arr:{}'.format(arr))
print ('arr shape:{}'.format(arr.shape))
b=[[1,2,3,4],[5,6,7,8]]
arr1=np.array(b)
print('arr1:{}'.format(arr1))
print ('arr1 shape:{}'.format(arr1.shape))
arr1_reshape=arr1.reshape(4,2)
print('arr1_reshape:{}'.format(arr1_reshape))
print('arr1:{}'.format(arr1))
arr1[0,0]=10
print('arr1:{}'.format(arr1))
print('arr1_reshape:{}'.format(arr1_reshape))
arr5=np.arange(15)
print('arr5:{}'.format(arr5))
print('arr5[8]:{}'.format(arr5[8]))
print('arr5[8:12]:{}'.format(arr5[8:12]))
arr6=np.arange(25).reshape(5,5)
print('arr6:{}'.format(arr6))#切片索引
print('arr6[1,2:4]:{}'.format(arr6[1,2:4]))
print('arr6[1:3,2：4]：{}'.format(arr6[1:3,2:4]))
print('arr6[:,2:]:{}'.format(arr6[:,2:]))
print('arr6[::2,2:]:{}'.format(arr6[::2,2:]))
x=np.array([0,1,2,3,1]) #布尔型索引
print('x:{}'.format(x))
print('x==1:{}'.format(x==1))
print('arr6[[False,True,False,False,True]]:{}'.format(arr6[[False,True,False,False,True]]))
print('arr6[x==1]:{}'.format(arr6[x==1]))
print('arr6[x!=1]:{}'.format(arr6[x != 1]))
print('arr6[~(x==1)]:{}'.format(arr6[~(x == 1)]))
print ('arr6.sum():{}'.format(arr6.sum())) #求和
print ('arr6.mean():{}'.format(arr6.mean())) #均值
print ('arr6.std():{}'.format(arr6.std())) #标准差
print ('arr6.var():{}'.format(arr6.var())) #方差
print ('arr6.max():{}'.format(arr6.max())) # 最大值
print ('arr6.min():{}'.format(arr6.min())) # 最小值
print ('arr6.cumsum():{}'.format(arr6.cumsum())) # 累计和
print ('arr6.cumprod():{}'.format(arr6.cumprod())) # 累计积
print ('arr6.argmin():{}'.format(arr6.argmin())) # 最小元素索引
print ('arr6.argmax():{}'.format(arr6.argmax())) # 最大元素索引
print ('arr6.T:{}'.format(arr6.T)) #矩阵转置
a = [ [1,0,0],
[0,2,0],
[0,0,3] ]
print (np.linalg.inv(a)) #逆矩阵
print ('arr6+arr6:{}'.format(arr6+arr6)) #矩阵的加法
print ('arr6-arr6:{}'.format(arr6 - arr6)) # 矩阵的减法
print ('矩阵6的点乘：{}'.format(arr6*arr6))
print ('矩阵6乘矩阵6：{}'.format(arr6.dot(arr6)))
print ('矩阵6的迹：{}'.format(np.trace(arr6)))
print ('矩阵6的特征值：{}'.format(np.linalg.eig(arr6)[0]))
print ('矩阵6的特征向量：{}'.format(np.linalg.eig(arr6)[1]))
7.padnas 测试
import pandas as pd
s1 = pd.Series([1, 2, 3, 4, 5], index=[1, 2, 3, 4, 5])
print('s1:{}'.format(s1))
s1 = pd.Series([1,2,3,4,5])
print('s1:{}'.format(s1))
print('s1索引:{}'.format(s1.index))
print('s1数值:{}'.format(s1.values))
df=pd.DataFrame([[3,2,np.nan,np.nan],[2,5,3,np.nan],[3,4,5,np.nan],[9,5,3,np.nan]],
index=['a','b','c','d'],columns=['one','two','three','four'])
print ('df:{}'.format(df))
print ('df按列求和:{}'.format(df.sum()))
print ('df按行求和:{}'.format(df.sum(axis=1)))
print ('df去除空值：{}'.format(df.dropna()))
print ('df条件去除空值：{}'.format(df.dropna(how='all',axis=1)))
df=df.dropna(how='all',axis=1)
print ('df用0补全空值：{}'.format(df.fillna(0)))
print ('df用中位数补全空值：{}'.format(df.fillna(df.median())))
print ('concat:{}'.format(pd.concat([df,df])))
print ('append:{}'.format(df.append(df)))
print ('merge:{}'.format(pd.merge(df, df,left_on='one',right_on='one',how='left')))
8.matplotlib测试
import numpy as np
import matplotlib.pyplot as plt
x=np.random.randn(1000)
y=np.random.randn(1000)
plt.scatter(x,y,color='g',marker='*',alpha=0.5)
plt.title("Scatter plot for 1000 random data from normal distrbution")
plt.show()
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