IEX Cloud API 出现“ValueError: year is out of range”

Question

I have a CSV (which I converted to a dataframe) consisting of company/stock data:

  Symbol  Quantity  Price  Cost      date
0    DIS         9    NaN    20  20180531
1   SBUX         5    NaN    30  20180228
2   PLOW         4    NaN    40  20180731
3   SBUX         2    NaN    50  20191130
4    DIS        11    NaN    25  20171031

And I am trying to use the IEX Cloud API to pull in the stock Price for a given date . And then ultimately write that to the dataframe. Per the IEX Cloud API documentation , I can use the get_historical_data function, where the 2nd argument is the date : df = get_historical_data("SBUX", "20190617", close_only=True)

Everything works fine so long as I pass in a raw date directly to the function (eg, 20190617 ), but if I try using a variable instead, I get ValueError: year 20180531 is out of range . I'm guessing something is wrong with the date format in my original CSV?

Here is my full code:

import os
from iexfinance.stocks import get_historical_data
import pandas as pd

os.environ['IEX_API_VERSION'] = 'iexcloud-sandbox'
os.environ['IEX_TOKEN'] = 'Tsk_5798c0ab124d49639bb1575b322841c4'

input_df = pd.read_csv("all.csv")

for index, row in input_df.iterrows():
    symbol = row['Symbol']
    date = row['date']
    temp_df = get_historical_data(symbol, date, close_only=True, output_format='pandas')
    price = temp_df['close'].values[0]

    print(temp_df)

Note that this is a public token, so it's okay to use

Answer 1

When you called get_historical_data("SBUX", "20190617", close_only=True) you passed the date as a string .

But when you read a DataFrame using read_csv , this column (containing 8-digit strings) is converted to an integer .

This difference can be the source of problem.

Try 2 things:

• convert this column to string, or
• while reading the DataFrame, pass dtype={'date': str} , so that this column will be read as a string.

Answer 2

You should be fine if you transform your date row into datetime .

import pandas as pd

df = pd.DataFrame(['20180531'])

pd.to_datetime(df.values[:, 0])

Out[43]: DatetimeIndex(['2018-05-31'], dtype='datetime64[ns]', freq=None)

Then, your column will be correctly formatted for use elsewhere. You can insert this line below pd.read_csv() :

df['date'] = pd.to_datetime(df['date'])