我试图通过 IEX API 循环一个股票代码列表，以获得大量股票代码的市账率。最终目标是在一个 csv 文件中包含所有的市账率。对于多个股票代码，它以我没有很多经验的 dicts dict 的形式返回资产负债表信息。
以下代码仅适用于资产负债表回报位于数据框中的单个代码。
import pandas as pd
from iexfinance.stocks import Stock
from datetime import datetime
import matplotlib.pyplot as plt
from iexfinance.stocks import get_historical_data
from pathlib import Path
import numpy as np
import csv
output_file = pd.DataFrame()
filename = r'C:\Users########'
Ticker = []
with open(filename, newline='') as inputfile:
for row in csv.reader(inputfile):
Ticker.append(row[0])
for i in Ticker:
#Obtain information for ticker company when using a single ticker
#Ticker = ['FRT']
#Obtaining Balance Sheets
stock = Stock(Ticker, token='##################', output_format='pandas')
bs = stock.get_balance_sheet()
#Obtaining the common stock outstanding on the last filing
common_stock_outstanding = stock.get_shares_outstanding()
#Obtaining the current price
current_price = stock.get_price()
#Obtaining the Market Cap
market_cap = common_stock_outstanding * current_price
#Obtaining the BV per share
bs['BV'] = bs['shareholderEquity'] / common_stock_outstanding
bs.drop(bs.iloc[:, 0:38], inplace = True, axis = 1)
BV = pd.DataFrame(bs)
BV.columns = ['1']
BV = BV.reset_index(drop=True)
#Obtaining the Price to Book
current_price = pd.DataFrame(current_price)
current_price = current_price.reset_index(drop=True)
current_price.columns = ['2']
current_price
PtB = pd.merge(BV, current_price, left_index=True, right_index=True)
PtB['PtB'] = PtB['2'] / PtB['1']
PtB.drop(PtB.iloc[:, 0:2], inplace = True, axis = 1)
PtB.columns = [Ticker]
#Appending the PtB into the Output File
output_file = pd.DataFrame()
output_file = output_file.append(PtB)
output_file
这是两个 Tickers RFP 和 APA 的返回输出：
{'RFP': accountsPayable capitalSurplus commonStock currency \
2021-03-01 251000000 None 82200000 USD
currentAssets currentCash currentLongTermDebt filingType \
2021-03-01 900000000 113000000 2000000 10-K
fiscalDate fiscalQuarter ... symbol totalAssets \
2021-03-01 2020-12-31 4 ... RFP 3730000000
totalCurrentLiabilities totalLiabilities treasuryStock \
2021-03-01 380000000 2649000000 -174000000
id key subkey date updated
2021-03-01 BALANCE_SHEET RFP quarterly 1609372800000 1614775188000
[1 rows x 38 columns],
'APA': accountsPayable capitalSurplus commonStock currency \
2020-07-30 459000000 None 12006000000 USD
currentAssets currentCash currentLongTermDebt filingType \
2020-07-30 1658000000 135000000 294000000 10-Q
fiscalDate fiscalQuarter ... symbol totalAssets \
2020-07-30 2020-06-30 2 ... APA 12999000000
totalCurrentLiabilities totalLiabilities treasuryStock \
2020-07-30 1710000000 14634000000 -3189000000
id key subkey date updated
2020-07-30 BALANCE_SHEET APA quarterly 1593475200000 1608330865000
[1 rows x 38 columns]}
我尝试使用 pd.DataFrame.from_dict(bs, orient='index') 将 dict 的 dict 放入数据帧，但收到错误：
KeyError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 0
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-17-b29d66f7b6fd> in <module>
----> 1 pd.DataFrame.from_dict(bs, orient='index')
~\Anaconda3\lib\site-packages\pandas\core\frame.py in from_dict(cls, data, orient, dtype, columns)
1188 raise ValueError("only recognize index or columns for orient")
1189
-> 1190 return cls(data, index=index, columns=columns, dtype=dtype)
1191
1192 def to_numpy(self, dtype=None, copy=False):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
462 mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
463 else:
--> 464 mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
465 else:
466 mgr = init_dict({}, index, columns, dtype=dtype)
~\Anaconda3\lib\site-packages\pandas\core\internals\construction.py in init_ndarray(values, index,
columns, dtype, copy)
169 # by definition an array here
170 # the dtypes will be coerced to a single dtype
--> 171 values = prep_ndarray(values, copy=copy)
172
173 if dtype is not None:
~\Anaconda3\lib\site-packages\pandas\core\internals\construction.py in prep_ndarray(values, copy)
274 try:
275 if is_list_like(values[0]) or hasattr(values[0], "len"):
--> 276 values = np.array([convert(v) for v in values])
277 elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
278 # GH#21861
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2978 if self.columns.nlevels > 1:
2979 return self._getitem_multilevel(key)
-> 2980 indexer = self.columns.get_loc(key)
2981 if is_integer(indexer):
2982 indexer = [indexer]
~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2897 return self._engine.get_loc(key)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2901 if indexer.ndim > 1 or indexer.size > 1:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 0
我知道代码的构造可能效率低下，但此时我只想了解如何使用 dicts 输出的这个 dict。必须有一种方法可以做到这一点，但目前这超出了我的技能范围。感谢您的帮助。
答案 0 :(得分：0)
在您提供试图从中创建数据框的确切字典之前，我认为我们无法帮助您。 无论如何，作为示例，请看以下示例：
>>data = {'name': {'first':'hary', 'second':'porter'}, 'address':{'temp': 'USA', 'perm': 'UK'}}
>>data
{'name': {'first': 'hary', 'second': 'porter'}, 'address': {'temp': 'USA', 'perm': 'UK'}}
>>pd.DataFrame(data)
name address
first hary NaN
second porter NaN
temp NaN USA
perm NaN UK
答案 1 :(得分：-1)
Dataframe 也带参数字典。
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
输出：
col1=1,2 col2 = 3,4