If you are interested, look into 'Exploratory Data Analysis' to start. As the name suggest, it is the first step in looking at your data. It usually consists of boxplots(think: std. dev. and outliers), histograms (distributions, data spread and outliers) and then some houskeeping (what is your data comprised of {categorical, nominal, numerical or continuous} or {character, list, etc}, data types.
I like Roger Peng, leanpub.com/exdata. Free, and easy intro but it is more R than Python but you can get the main ideas easily.
For the next step after that let me know and I can suggest more...
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If you are interested, look into 'Exploratory Data Analysis' to start. As the name suggest, it is the first step in looking at your data. It usually consists of boxplots(think: std. dev. and outliers), histograms (distributions, data spread and outliers) and then some houskeeping (what is your data comprised of {categorical, nominal, numerical or continuous} or {character, list, etc}, data types.
I like Roger Peng, leanpub.com/exdata. Free, and easy intro but it is more R than Python but you can get the main ideas easily.
For the next step after that let me know and I can suggest more...