如果您打算投资股票，那么快速查看个别历史股票价格绝对是一个好主意。第一步是下载历史价格 - 但交易所网站不允许您有效地提取所有股票价格的数据。以下列出的是为NSE（印度国家证券交易所）上市的所有股票提供日常股票价格数据的主要步骤：

导入模块 - 熊猫，numpy，日期时间，csv，pandas_datareader

# Import modules

from datetime import datetime, timedelta

import pandas as pd

pd.core.common.is_list_like = pd.api.types.is_list_like #For solving import pandas_datareader issue

import numpy as np

import datetime

import csv

import requests

import pandas_datareader.data as web

import pandas_datareader as pdr

from pandas_datareader import data, wb

输入我们想要股票价格的开始和结束日期

# Input Start and End Date

start = datetime.datetime(2018,4,20)

end = datetime.datetime(2018,5,20)

提取NSE上列出的所有股票代码，例如20MICRONS，RELIANCE，BOSCHLTD

Today = datetime.datetime.now().strftime ("%Y-%m-%d")

# Import list of stock names from NSE website

with requests.Session() as s:

download = s.get('https://www.nseindia.com/products/content/sec_bhavdata_full.csv')

decoded_content = download.content.decode('utf-8')

cr = csv.reader(decoded_content.splitlines(), delimiter=',')

my_list = pd.DataFrame(list(cr))

#View the top rows

my_list.head()

# Clean the downloaded data

# Rename the headers

new_header = my_list.iloc[0] #grab the first row for the header

my_list = my_list[1:] #take the data less the header row

my_list = my_list.rename(columns = new_header)

# Get only the list of stock names - remove everything else

my_list['stock_name'] = "NSE/"+ my_list['SYMBOL']

stock_list = my_list['stock_name'].tolist()

stock_list = list(set(stock_list))

#View the top few stock names

stock_list[1:10]

输出：

['NSE/HARRMALAYA',

'NSE/ANSALHSG',

'NSE/DPL',

'NSE/STAN',

'NSE/VOLTAMP',

'NSE/SUMEETINDS',

'NSE/JKPAPER',

'NSE/KPRMILL',

'NSE/INEOSSTYRO']

运行for循环以下载每个股票代码的股票价格数据。

在for循环中，我们将使用web.Datareader（）函数从quandl（https://www.quandl.com/）下载数据

# Create Empty Dataframe

stock_final = pd.DataFrame()

# Scrape the stock prices from quandl

# for i in range(len(stock_list))#Use this to scrape all the stocks

for i in range(10):

print(i)

try:

stock=[]

stock = web.DataReader(stock_list[i],"quandl",start,end)

stock['Name']=stock_list[i]

stock_final = pd.DataFrame.append(stock_final,stock)

except Exception: # Replace Exception with something more specific.

i = i+1

清理并附加数据，我们应该有以下分析数据集：

#View the top 10 rows of the downloaded data

stock_final.head()

我们可以使用这些数据来执行我们所需的所有分析和可视化

#Plot trend for a particular stock

#Subset for a particular stock

stock_final = stock_final[stock_final['Name']=='NSE/SUTLEJTEX']

#Generate a line plot

stock_final.plot(y='High')

plt.show()

“SUTLEJTEX”股票的价格走势