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---
title: Working with Data
teaching: 20
exercises: 10
questions:
- "How should I work with numeric data in Python?"
- "What's the recommended way to handle and analyse tabular data?"
- "How can I import tabular data for analysis in Python and export the results?"
objectives:
- "handle and summarise numeric data with Numpy."
- "filter values in their data based on a range of conditions."
- "load tabular data into a Pandas dataframe object."
- "describe what is meant by the data type of an array/series, and the impact this has on how the data is handled."
- "add and remove columns from a dataframe."
- "select, aggregate, and visualise data in a dataframe."
keypoints:
- "Specialised third-party libraries such as Numpy and Pandas provide powerful objects and functions that can help us analyse our data."
- "Pandas dataframe objects allow us to efficiently load and handle large tabular data."
- "Use the `pandas.read_csv` and `pandas.write_csv` functions to read and write tabular data."
---
## plan
- Toby currently scheduled to lead this session
- Numpy
- arrays
- masking
- aside about data types and potential hazards
- reading data from a file (with note that more will come later on this topic)
- link to existing image analysis material
- Pandas
- when an array just isn't enough
- DataFrames - re-use material from [Software Carpentry][swc-python-gapminder]?
- ideally with a more relevant example dataset... [maybe a COVID one](https://data.europa.eu/euodp/en/data/dataset/covid-19-coronavirus-data/resource/260bbbde-2316-40eb-aec3-7cd7bfc2f590)
- include an aside about I/O - reading/writing files (pandas (the `.to_*()` methods and highlight some: `csv`, `json`, `feather`, `hdf`), numpy, `open()`, (?) bytes vs strings, (?) encoding)
- Finish with example of `df.plot()` to set the scene for plotting section
> ## Reading data to a numpy array
> We'll use the popular image analysis package scikit-image, to read two example images into numpy
> arrays.
> > ~~~
> > from skimage.io import imread
> > raw = imread('cilliated_cell.png')
> > nuclei = imread('cilliated_cell_nuclei.png')
> > # if you want to see what these images look like - we can use matplotlib (more to come later!)
> > import matplotlib.pyplot as plt
> > plt.imshow(raw, cmap='gray')
> > plt.imshow(nuclei)
> > ~~~
> > {: .language-python }
> * What are the dimensions of these arrays?
> * What data type are these arrays?
> * What is the minimum and maximum value of these arrays?
> > ## Solution
> > ~~~
> > print(raw.shape)
> > print(raw.dtype)
> > print(np.max(raw))
> > print(np.min(raw))
> > ~~~
> > {: .language-python }
> {: .solution }
{: .challenge }