Timestamp not showing up in yfinance package in Python

user_8282670 0
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I am trying to pull stock price history at 1 hour intervals through the Yahoo Finance API using the yfinance package. I run the following code.

import yfinance as yf
msft = yf.Ticker("MSFT")
df = msft.history(period = "5d", interval = "1h")
df.reset_index(inplace = True)
print(df["Date"][0])
print(df["Date"][1])
print(df["Date"][2])

I get the output

2020-04-03 00:00:00
2020-04-03 00:00:00
2020-04-03 00:00:00

Why are the timestamps all 00:00:00? The stock prices are actually at 1 hour intervals and seem correct. The dates also change correctly after 7 rows. Just the timestamps are all 0s. I can just postprocess the timestamps as I know the intervals. Just curious if I am doing something wrong here. Is this how the package is supposed to work?

yfinance pandas python
2 Answers
user_34564460

To those who are new to yfinance this is how to extract the data from the yfinance history() function in more detail.

yfinance uses a module called Pandas. The data structures returned from the yfinance API are Pandas objects.

The object returned by the history() function is a Pandas DataFrame object. They are like 2 dimensional arrays, with extras. For DataFrame objects, there is a columns field which contains an array of column names, and an index field which contains an array of index objects applicable to the columns. The indexes are of a fixed type, and can be objects themselves. In the DataFrame object returned by the yfinance history() function, the indexes are Pandas Timestamp objects. (Pandas allows using any type for the indexes, for example plain integers or strings or other objects would be also allowed)

There is an in-depth description of Pandas datastructures here and here.

Each column in the DataFrame object is a Pandas Series object which is like a one dimensional array. The columns can be accessed by the column names from the DataFrame object. The column values in each column can be accessed using the index objects. Every column uses the same indexes. The Python array notation [ ] can be used to access the fields in the Pandas objects.

This is how to access the data:

def zeroX(n):
    result = ""
    if (n < 10):
        result += "0"
    result += str (n)
    return result

def dump_Pandas_Timestamp (ts):
    result = ""
    result += str(ts.year) + "-" + zeroX(ts.month) + "-" + zeroX(ts.day)
    #result += " " + zeroX(ts.hour) + ":" + zeroX(ts.minute) + ":" + zeroX(ts.second)
    return result

def dump_Pandas_DataFrame (DF):
    result = ""
    for indexItem in DF.index:
        ts = dump_Pandas_Timestamp (indexItem)
        fields = ""
        first = 1
        for colname in DF.columns:
            fields += ("" if first else ", ") + colname + " = " + str(DF[colname][indexItem])
            first = 0
        result += ts + " " + fields + "\n"
    return result
    
msft = yf.Ticker("MSFT")
    
# get historical market data
hist = msft.history(period="1mo", interval="1d")
    
print ("hist = " + dump_Pandas_DataFrame(hist))

Output:

hist = 2020-07-08 Open = 210.07, High = 213.26, Low = 208.69, Close = 212.83, Volume = 33600000, Dividends = 0, Stock Splits = 0
2020-07-09 Open = 216.33, High = 216.38, Low = 211.47, Close = 214.32, Volume = 33121700, Dividends = 0, Stock Splits = 0
2020-07-10 Open = 213.62, High = 214.08, Low = 211.08, Close = 213.67, Volume = 26177600, Dividends = 0, Stock Splits = 0
2020-07-13 Open = 214.48, High = 215.8, Low = 206.5, Close = 207.07, Volume = 38135600, Dividends = 0, Stock Splits = 0
2020-07-14 Open = 206.13, High = 208.85, Low = 202.03, Close = 208.35, Volume = 37591800, Dividends = 0, Stock Splits = 0
2020-07-15 Open = 209.56, High = 211.33, Low = 205.03, Close = 208.04, Volume = 32179400, Dividends = 0, Stock Splits = 0
2020-07-16 Open = 205.4, High = 205.7, Low = 202.31, Close = 203.92, Volume = 29940700, Dividends = 0, Stock Splits = 0
2020-07-17 Open = 204.47, High = 205.04, Low = 201.39, Close = 202.88, Volume = 31635300, Dividends = 0, Stock Splits = 0
2020-07-20 Open = 205.0, High = 212.3, Low = 203.01, Close = 211.6, Volume = 36884800, Dividends = 0, Stock Splits = 0
2020-07-21 Open = 213.66, High = 213.94, Low = 208.03, Close = 208.75, Volume = 38105800, Dividends = 0, Stock Splits = 0
2020-07-22 Open = 209.2, High = 212.3, Low = 208.39, Close = 211.75, Volume = 49605700, Dividends = 0, Stock Splits = 0
2020-07-23 Open = 207.19, High = 210.92, Low = 202.15, Close = 202.54, Volume = 67457000, Dividends = 0, Stock Splits = 0
2020-07-24 Open = 200.42, High = 202.86, Low = 197.51, Close = 201.3, Volume = 39827000, Dividends = 0, Stock Splits = 0
2020-07-27 Open = 201.47, High = 203.97, Low = 200.86, Close = 203.85, Volume = 30160900, Dividends = 0, Stock Splits = 0
2020-07-28 Open = 203.61, High = 204.7, Low = 201.74, Close = 202.02, Volume = 23251400, Dividends = 0, Stock Splits = 0
2020-07-29 Open = 202.5, High = 204.65, Low = 202.01, Close = 204.06, Volume = 19632600, Dividends = 0, Stock Splits = 0
2020-07-30 Open = 201.0, High = 204.46, Low = 199.57, Close = 203.9, Volume = 25079600, Dividends = 0, Stock Splits = 0
2020-07-31 Open = 204.4, High = 205.1, Low = 199.01, Close = 205.01, Volume = 51248000, Dividends = 0, Stock Splits = 0
2020-08-03 Open = 211.52, High = 217.64, Low = 210.44, Close = 216.54, Volume = 78983000, Dividends = 0, Stock Splits = 0
2020-08-04 Open = 214.17, High = 214.77, Low = 210.31, Close = 213.29, Volume = 49280100, Dividends = 0, Stock Splits = 0
2020-08-05 Open = 214.9, High = 215.0, Low = 211.57, Close = 212.94, Volume = 28858600, Dividends = 0, Stock Splits = 0
2020-08-06 Open = 212.34, High = 216.37, Low = 211.55, Close = 216.35, Volume = 32656800, Dividends = 0, Stock Splits = 0
2020-08-07 Open = 214.85, High = 215.7, Low = 210.93, Close = 212.48, Volume = 27789600, Dividends = 0, Stock Splits = 0
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