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isaacslavitt.com | ||
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janakiev.com
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| | | | | One of the crucial tasks when working with data is to load data properly. The common way the data is formated is CSV, which comes in different flavors and varying difficulties to parse. This article shows three common approaches in Python. | |
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tomaugspurger.net
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| | | | | This is part 7 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Timeseries Pandas started out in the financial world, so naturally it has strong timeseries support. The first half of this post will look at pandas' capabilities for manipulating time series data. The second half will discuss modelling time series data with statsmodels. %matplotlib inline import os import numpy as np import pandas as pd import pandas_datareader.data as web import seaborn as sns import matplotlib.pyplot as plt sns.set(style='ticks', context='talk') if int(os.environ.get("MODERN_PANDAS_EPUB", 0)): import prep # noqa Let's grab some stock data for Goldman Sachs using the pandas-datareader package, which spun off of pandas: | |
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tomaugspurger.net
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| | | | | This is part 2 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Method Chaining Method chaining, where you call methods on an object one after another, is in vogue at the moment. Its always been a style of programming thats been possible with pandas, and over the past several releases, weve added methods that enable even more chaining. | |
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www.joeltok.com
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| | | Python dependency management for reproducibility, using built-in libraries. | ||
