Importing and Analyzing Financial Time Series Data with Pandas

The article demonstrates how to load a local CSV file containing financial time‑series data into a pandas DataFrame, configure the index as a DatetimeIndex, and perform basic exploratory analysis.

import numpy as np
import pandas as pd
from pylab import mpl, plt
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline

Data is read with pd.read_csv(filename, index_col=0, parse_dates=True) , after which data.info() and data.describe() provide structural and statistical overviews.

data.head()
data.tail()
data.plot(figsize=(10,12), subplots=True)

Absolute changes are computed using data.diff() , and rate of change (simple returns) with data.pct_change() , which can be visualized as bar charts.

data.diff().head()
data.pct_change().round(3).head()
data.pct_change().mean().plot(kind='bar', figsize=(10,6))

Log returns are calculated via rets = np.log(data / data.shift(1)) and cumulative log returns plotted after exponentiation.

rets = np.log(data / data.shift(1))
rets.cumsum().apply(np.exp).plot(figsize=(10,6))

Resampling techniques such as weekly and monthly down‑sampling are shown using data.resample('1w', label='right').last() and data.resample('1m', label='right').last() , with attention to avoiding look‑ahead bias by using right‑label alignment.

data.resample('1w', label='right').last().head()
data.resample('1m', label='right').last().head()