pandas is the Python tool nearly everyone in finance reaches for when dealing with tables of data. A quant uses it to load prices, do math across whole rows, and look for patterns in seconds rather than hours.
About 2 min of reading
A pandas table is called a DataFrame. Rows are usually days, and columns are things like open, high, low, and close. You grab a column by its name, and everything else happens on top of that.
pandas does in seconds what takes minutes in Excel, and keeps working when the data gets too big for the spreadsheet to even open. A quant can pull years of prices and summarize the whole thing in a few lines.
import pandas as pd
prices = pd.DataFrame({
'date': ['2024-01-01', '2024-01-02'],
'close': [150, 153]
})
print(prices['close'].mean()) # average priceThat same line of code does the exact thing whether you hand it two prices or two million. pandas was built for the moment a spreadsheet stops keeping up.
import pandas as pd
df = pd.read_csv('spy.csv')
df['return'] = df['close'].pct_change()
print(df['return'].mean())Turning prices into returns, the percentage change each day, is one of the most common first steps in this field. It happens in a single line, which is what lets the analysis run fast enough to actually be useful.
print(df['return'].std()) # volatility
print(df['return'].max()) # best dayA whole year of data, compressed into a single volatility number or a single best day. That's how a quant measures risk without squinting at a spreadsheet of thousands of cells.
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