Filtering data
Intermediate Python for Finance
Kennedy Behrman
Data Engineer, Author, Founder
Introducing the data
prices.head()
Introducing the data
prices.head()
| Date | Symbol | High | |
|---|---|---|---|
| 0 | 2020-04-03 | AAPL | 245.70 |
| 1 | 2020-04-02 | AAPL | 245.15 |
| 2 | 2020-04-01 | AAPL | 248.72 |
| 3 | 2020-03-31 | AAPL | 262.49 |
| 4 | 2020-03-30 | AAPL | 255.52 |
Introducing the data
prices.describe()
Introducing the data
prices.describe()
| High | |
|---|---|
| count | 378.000000 |
| mean | 881.593138 |
| std | 720.771922 |
| min | 227.490000 |
| max | 2185.950000 |
Introducing the data
prices.describe(include='object')
| Symbol | |
|---|---|
| count | 378 |
| unique | 3 |
| top | AMZN |
| freq | 126 |
Comparison operators
< <= > >= == !=
Column comparison
prices.High > 2160
Column comparison
prices.High > 2160
0 False
1 False
2 False
3 False
4 False
...
374 False
375 False
376 False
377 False
Column comparison
prices.Symbol == 'AAPL'
Column comparison
prices.Symbol == 'AAPL'
0 True
1 True
2 True
3 True
4 True
...
374 False
375 False
376 False
377 False
Masking by symbol
mask_symbol = prices.Symbol == 'AAPL'
aapl = prices.loc[mask_symbol]
Masking by symbol
mask_symbol = prices.Symbol == 'AAPL'
aapl = prices.loc[mask_symbol]
aapl.describe(include='object')
| Symbol | |
|---|---|
| count | 126 |
| unique | 1 |
| top | AAPL |
| freq | 126 |
Masking by price
mask_high = prices.High > 2160
big_price = prices.loc[mask_high]
Masking by price
big_price.describe()
| High | |
|---|---|
| count | 6.000000 |
| mean | 2177.406567 |
| std | 7.999334 |
| min | 2166.070000 |
| max | 2185.95000 |
Pandas Boolean operators
- And
& - Or
| - Not
~
Combining conditions
mask_prices = prices['Symbol'] != 'AMZN'
mask_date = historical_highs['Date'] > datetime(2020, 4, 1)
mask_amzn = mask_prices & mask_date
prices.loc[mask_amzn]
Combining conditions
| Date | Symbol | High | |
|---|---|---|---|
| 0 | 2020-04-03 | AAPL | 245.7000 |
| 1 | 2020-04-02 | AAPL | 245.1500 |
| 252 | 2020-04-03 | TSLA | 515.4900 |
| 253 | 2020-04-02 | TSLA | 494.2599 |
Let's practice!
Intermediate Python for Finance
