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我打算使用纳斯达克的数据做一些金融研究和学习。无法获得帮助从Python中的纳斯达克检索数据
我想要从纳斯达克数据，使得头部有以下几点：
股票代码
公司名称
上次出售
市值
IPO 年
部门
行业
最后更新
我用Python代码来获得使用“企业和股票名单”：
import pandas as pd
import json
PACKAGE_NAME = 'nasdaq-listings'
PACKAGE_TITLE = 'Nasdaq Listings'
nasdaq_listing = 'ftp://ftp.nasdaqtrader.com/symboldirectory/nasdaqlisted.txt'# Nasdaq only
def process():
nasdaq = pd.read_csv(nasdaq_listing,sep='|')
nasdaq = _clean_data(nasdaq)
# Create a few other data sets
nasdaq_symbols = nasdaq[['Symbol','Company Name']] # Nasdaq w/ 2 columns
# (dataframe, filename) datasets we will put in schema & create csv
datasets = [(nasdaq,'nasdaq-listed'), (nasdaq_symbols,'nasdaq-listed-symbols')]
for df, filename in datasets:
df.to_csv('data/' + filename + '.csv', index=False)
with open("datapackage.json", "w") as outfile:
json.dump(_create_datapackage(datasets), outfile, indent=4, sort_keys=True)
def _clean_data(df):
# TODO: do I want to save the file creation time (last row)
df = df.copy()
# Remove test listings
df = df[df['Test Issue'] == 'N']
# Create New Column w/ Just Company Name
df['Company Name'] = df['Security Name'].apply(lambda x: x.split('-')[0]) #nasdaq file uses - to separate stock type
#df['Company Name'] = TODO, remove stock type for otherlisted file (no separator)
# Move Company Name to 2nd Col
cols = list(df.columns)
cols.insert(1, cols.pop(-1))
df = df.loc[:, cols]
return df
def _create_file_schema(df, filename):
fields = []
for name, dtype in zip(df.columns,df.dtypes):
if str(dtype) == 'object' or str(dtype) == 'boolean': # does datapackage.json use boolean type?
dtype = 'string'
else:
dtype = 'number'
fields.append({'name':name, 'description':'', 'type':dtype})
return {
'name': filename,
'path': 'data/' + filename + '.csv',
'format':'csv',
'mediatype': 'text/csv',
'schema':{'fields':fields}
}
def _create_datapackage(datasets):
resources = []
for df, filename in datasets:
resources.append(_create_file_schema(df,filename))
return {
'name': PACKAGE_NAME,
'title': PACKAGE_TITLE,
'license': '',
'resources': resources,
}
process()
现在对于每个符号，我希望得到其他数据（如上述）。
无论如何我可以做到这一点吗？