So I'm an economics undergrad trying to learn some pandas for data manipulation.

# Making YEAR as the index
inflation_forecasts.index = pd.DatetimeIndex(inflation_forecasts['YEAR'].astype(int).astype(str)+'-Q'+inflation_forecasts['QUARTER'].astype(int).astype(str),freq = 'QS')

This is from my professor's notebook. The code downloads macroeconomic data from a public database as an excel file and now we're converting it into a df. I'm good up to this point but I don't get why .astype(str) follows .astype(int)

I tried looking up pandas manipulation but all it did was reaffirm what the astype method does which is converts the dtype into the specified type. does this line of code 1.converts values in YEAR and QUARTER into an integer THEN a string? - Why would you do this ??- 2.concatenates YEAR and QUARTER into one 'column'

So I'm an economics undergrad trying to learn some pandas for data manipulation.

# Making YEAR as the index
inflation_forecasts.index = pd.DatetimeIndex(inflation_forecasts['YEAR'].astype(int).astype(str)+'-Q'+inflation_forecasts['QUARTER'].astype(int).astype(str),freq = 'QS')

This is from my professor's notebook. The code downloads macroeconomic data from a public database as an excel file and now we're converting it into a df. I'm good up to this point but I don't get why .astype(str) follows .astype(int)

I tried looking up pandas manipulation but all it did was reaffirm what the astype method does which is converts the dtype into the specified type. does this line of code 1.converts values in YEAR and QUARTER into an integer THEN a string? - Why would you do this ??- 2.concatenates YEAR and QUARTER into one 'column'

• python
• pandas
• dataframe
• methods

• 1 What's the value of inflation_forecasts['YEAR'].dtype prior to this step? The one purpose I can imaging is that if inflation_forecasts['YEAR'] were a floating point number, then converting from float to string would produce a year like 2024.0, but converting from float to int to string would produce a year like 2024. I'd have to know the original dtype to know if that was the purpose, though. – Nick ODell Commented Jan 30 at 19:18
• @NickODell YEAR and QUARTER are int64. But your explanation makes sense ty. It's probably there just in case other data sources run into that problem. – Foodle Jang Commented Jan 30 at 19:25

1 Answer 1

Yes, the code converts YEAR and QUARTER into integers first, then to strings.

1. .astype(int) ensures that the values are treated as integers, which removes any potential non-numeric values or floating points.
2. .astype(str) then converts them into strings so that they can be concatenated properly into a single string, forming the date in the 'YYYY-QX' format.

The line concatenates YEAR and QUARTER into one string column. It combines the YEAR and QUARTER columns to create a single string representing the quarter (e.g., '2021-Q1'), which is then used as the index for the DataFrame.