Pandas

The other day, I was grabbing a way-too-big DB query for local exploration.  It was literally over 2GB as a CSV - which is a pain for a number of reasons!  Not the least of which being that, while you're doing Exploratory Data Analysis, things take way too long - it doesn't take long for Cognitive Drift to break your rhythm!

Numerical columns can be taken down to size with the downcasting functions from a previous post.  But what about Object/String columns?  One of the best ways to reduce the size of a file like this is to recast

Sometimes you wind up with duplicate column names in your Pandas DataFrame. This isn't necessarily a huge deal if we're just messing with a smallish file in Jupyter.  But, if we wanna do something like load it into a database, that'll be a problem.  It can also interfere with our other cleaning functions - I ran into this the other day when reducing the size of a giant data file by downcasting it (as per this previous post).  The cleaning functions required a 1D input (so, a Series or List) - but calling the name of a duplicate column gave

Recently, I had to find a way to reduce the memory footprint of a Pandas DataFrame in order to actually do operations on it.  Here's a trick that came in handy!

By default, if you read a DataFrame from a file, it'll cast all the numerical columns as the float64 type.  This is in keeping with the philosophy behind Pandas and NumPy - by using strict types (instead of normal Python "duck typing"), you can do things a lot faster.  The float64 is the most flexible numerical type - it can handle fractions, as well as turning missing values into

What if, like during my data import task a few months back, the dates & times are in separate columns?  This gives us a few new issues.  Let's import that Excel file!

import pandas as pd
import xlrd
import datetime

df = pd.read_excel("hasDatesAndTimes.xlsx", sheet_name="Sheet1")

book = xlrd.open_workbook("hasDatesAndTimes.xlsx")
datemode = book.datemode

And let's see that time variable!

df["Time"]

df["Time"].map(lambda x: xlrd.xldate_

Different file formats are different!  For all kinds of reasons!

A few months back, I had to import some Excel files into a database. In this process I learned so much about the delightfully unique way Excel stores dates & times!  

The basic datetime will be a decimal number, like 43324.909907407404.  The number before the decimal is the day, the number afterwards is the time.  So far, so good - this is pretty common for computers.  The date is often the number of days past a certain date, and the time is the number of seconds.  

So, let's load

Ah, laziness.  You love it, I love it, everyone agrees it's just better.

Flesh-and-blood are famously lazy.  Pandas the package, however, uses Eager Evaluation.  What's Eager Evaluation, you ask?  Is Pandas really judgey, hanging out on the street corner and being fierce to the style choices of people walking by?  Well, yes, but that's not the most relevant sense in which I mean it here.  

Eager evaluation means that once you call pd.read_csv(), Pandas immediately jumps to read the whole CSV into memory.

"Wait!" I hear you ask.  "Isn't that what we want?  Why would I call the

Last few Code Snippet Corners were about using Pandas as an easy way to handle input and output between files & databases.  Let's shift gears a little bit!  Among other reasons, because earlier today I discovered a package that exclusively does that, which means I can stop importing the massive Pandas package when all I really wanted to do with it was take advantage of its I/O modules.  Check it out!

So, rather than the entrances & exits, let's focus on all the crazy ways you can reshape data with Pandas!

Our Data

For our demonstration, I'll use a

Let's say you were continuing our task from last week: Taking a bunch of inconsistent Excel files and CSVs, and putting them into a database.

Let's say you've been given a new CSV that conflicts with some rows you've already entered, and you're told that these rows are the correct values.

Why Not Use Pandas' Built-in Method?

Pandas' built-in to_sql DataFrame method won't be useful here.  Remember, it writes as a block - if you set the if_exists flag to "replace", that'll make it replace the entire DB table with a new one based on the DF you're