# Stock Market Data And Analysis In Python – Part IV

Articles From: QuantInsti
Website: QuantInsti

See Part I for instructions on how to get `pandas_datareader` or `yfinance` module to retrieve the data, Part II to learn how to get stock market data for different geographies, and Part III for a tutorial on how to analyse the stock market data for all the stocks which make up the S&P 500.

## Resample Stock Data

Convert 1-minute data to 1-hour data or Resample Stock Data

During strategy modelling, you might be required to work with a custom frequency of stock market data such as 15 minutes or 1 hour or even 1 month.

If you have minute level data, then you can easily construct the 15 minutes, 1 hour or daily candles by resampling them. Thus, you don’t have to buy them separately.

In this case, you can use the pandas resample method to convert the stock market data to the frequency of your choice. The implementation of these is shown below where a 1-minute frequency data is converted to 10-minute frequency data.

The first step is to define the dictionary with the conversion logic. For example, to get the open value the first value will be used, to get the high value the maximum value will be used and so on.

The name Open, High, Low, Close and Volume should match the column names in your dataframe.

``````ohlcv_dict = {
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum'
}``````

Convert the index to datetime timestamp as by default string is returned. Then call the resample method with the frequency such as:

• 10T for 10 minutes,
• D for 1 day and
• M for 1 month

``````# Import package & get the data
import yfinance as yf
period="5d",
interval="1m",

# Define the resampling logic
ohlcv_dict = {
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum'
}

# Resample the data

``````
``resample_data_10.py hosted with ❤ by GitHub``

Yahoo finance has limited set of minute level data. if you need the stock market data for higher range then you can get the data from data vendors such as Quandl, AlgoSeek or your broker.

Stay tuned for next installment in which Ishan Shah will demonstrate using Quandl to get Stock Market Data.

See https://blog.quantinsti.com/stock-market-data-analysis-python/ for additional insight on this topic.

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