Selecting data from data range
Pulling data that meets specific conditions is one of the most powerful and commonly used operations with DataFrames. You can try it now with Alphabet stock data. Provided is the DataFrame alphabet and the datetimes start_date and end_date. The DataFrame's head looks like this:
| date | close | volume | open | high | low |
|---|---|---|---|---|---|
| 2019-08-02 | 1196.32 | 1745450 | 1203.00 | 1209.500 | 1190.00 |
| 2019-08-01 | 1211.78 | 1771271 | 1217.63 | 1236.298 | 1207.00 |
| 2019-07-31 | 1218.20 | 1997999 | 1224.87 | 1234.910 | 1208.18 |
| 2019-07-30 | 1228.00 | 1430775 | 1227.00 | 1236.910 | 1225.32 |
| 2019-07-29 | 1241.84 | 2069127 | 1242.50 | 1248.995 | 1230.20 |
Este ejercicio forma parte del curso
Intermediate Python for Finance
Instrucciones del ejercicio
- Create a mask of historical dates in the given date range.
- A mask can be used to make a selection of rows from a DataFrame.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Calculate the mask for one week
mask = (alphabet['date'] ____ start_date) & (alphabet['____'] <= end_date)
# Use the mask to get the data for one week
df = alphabet[____]
# Look at result
print(df)