Overview

Teaching: 30 min
Exercises: 30 min

Questions

• How can I access specific data within my data set?

• How can Python and Pandas help me to analyse my data?

Objectives

• Describe what 0-based indexing is.

• Manipulate and extract data using column headings and index locations.

• Employ slicing to select sets of data from a DataFrame.

• Employ label and integer-based indexing to select ranges of data in a dataframe.

• Reassign values within subsets of a DataFrame.

• Create a copy of a DataFrame.

• Query /select a subset of data using a set of criteria using the following operators: =, !=, >, <, >=, <=.

• Locate subsets of data using masks.

• Describe BOOLEAN objects in Python and manipulate data using BOOLEANs.

In lesson 17, 18 and 19, we read a CSV into a Python pandas DataFrame. We learned:

• how to save the DataFrame to a named object,
• how to perform basic math on the data,
• how to calculate summary statistics, and
• how to clean up messy data output

In this lesson, we will explore ways to access different parts of the data using:

• indexing,
• slicing, and
• subsetting.

Loading our data

First, we’ll restart a Python environment with all our packages loaded.

source activate py36

We will continue to use the poretools output dataset that we worked with in the last lesson. Let’s reopen and read in the data again:

# Make sure pandas is loaded
import pandas as pd

# Read in the survey CSV
sep_data = pd.read_csv("column_separated.tsv", delimiter = "\t", index_col=0)

Indexing and Slicing in Python

We often want to work with subsets of a DataFrame object. There are different ways to accomplish this including: using labels (column headings), numeric ranges, or specific x,y index locations.

Selecting data using Labels (Column Headings)

We use square brackets [] to select a subset of an Python object. For example, we can select all data from a column named time from the sep_data DataFrame by name. There are two ways to do this:

# TIP: use the .head() method we saw earlier to make output shorter
# Method 1: select a 'subset' of the data using the column name
sep_data['time']

# Method 2: use the column name as an 'attribute'; gives the same output
sep_data.time

We can also create a new object that contains only the data within the time column as follows:

# Creates an object, surveys_species, that only contains the `time` column
time_stamps= sep_data['time']

We can pass a list of column names too, as an index to select columns in that order. This is useful when we need to reorganize our data.

NOTE: If a column name is not contained in the DataFrame, an exception (error) will be raised.

# Select the species and plot columns from the DataFrame
time_chan= sep_data[['time','channel']]

# What happens if you ask for a column that doesn't exist?
time_chan= sep_data[['time','channels']]

Python tells us what type of error it is in the traceback, at the bottom it says KeyError: 'channels' which means that channels is not a column name (or Key in the related python data type dictionary).

Extracting Range based Subsets: Slicing

REMINDER: Python Uses 0-based Indexing

Let’s remind ourselves that Python uses 0-based indexing. This means that the first element in an object is located at position

1. This is different from other tools like R and Matlab that index elements within objects starting at 1.

# Create a list of numbers:
a = [1, 2, 3, 4, 5]

Challenge - Extracting data

1. What value does the code below return?

   a[0]
   

2. How about this:

   a[5]
   

3. In the example above, calling a[5] returns an error. Why is that?

4. What about?

   a[len(a)]
   

Slicing Subsets of Rows in Python

Slicing using the [] operator selects a set of rows and/or columns from a DataFrame. To slice out a set of rows, you use the following syntax: data[start:stop]. When slicing in pandas the start bound is included in the output. The stop bound is one step BEYOND the row you want to select. So if you want to select rows 0, 1 and 2 your code would look like this:

# Select rows 0, 1, 2 (row 3 is not selected)
sep_data[0:3]

The stop bound in Python is different from what you might be used to in languages like Matlab and R.

# Select the first 5 rows (rows 0, 1, 2, 3, 4)
sep_data[:5]

# Select the last element in the list
# (the slice starts at the last element, and ends at the end of the list)
sep_data[-1:]

We can also reassign values within subsets of our DataFrame.

But before we do that, let’s look at the difference between the concept of copying objects and the concept of referencing objects in Python.

Slicing Subsets of Rows and Columns in Python

We can select specific ranges of our data in both the row and column directions using either label or integer-based indexing.

• loc is primarily label based indexing. Integers may be used but they are interpreted as a label.
• iloc is primarily integer based indexing

To select a subset of rows and columns from our DataFrame, we can use the iloc method. For example, we can select month, day and year (columns 2, 3 and 4 if we start counting at 1), like this:

# iloc[row slicing, column slicing]
sep_data.iloc[0:3, 1:4]

which gives the output

                                            sequence  \
0  TTATTGTAGTCGGTGGTGTGGCGGGTTGACTGAACTTGCTGCTTTT...   
1  TTGTTGGCACTTCGTTTCAGTTCTGCGGTGCTGGGCGGCGACCTCG...   
2  GGATTTCAGTTGCATGTTACTTATCCAAATTGTGTTTGGTTAGTCG...   

                                               quals  \
0  )""'$#$#&&'+)-&,%%%*&%*+(,*%%)*,-'+*+,.//*($%$...   
1  $&%'*'&$%'&(#(+410&+*,$#$$%')()219622522369343...   
2  +-&7.)*3537&+%+$4'%%%$(*5)+)*-,*.,,.*'**1.19+'...   

                                readName  
0  @bf327144-9003-4b2d-bad5-4cb380f40e8d  
1  @f54a0ac6-3563-432f-a447-60e4c95efb79  
2  @43a565b2-0dcb-4ea6-a32d-3984c3746e47 

Notice that we asked for a slice from 0:3. This yielded 3 rows of data. When you ask for 0:3, you are actually telling Python to start at index 0 and select rows 0, 1, 2 up to but not including 3.

Let’s explore some other ways to index and select subsets of data:

# Select all columns for rows of index values 0 and 10
sep_data.loc[[0, 10], :]

# What does this do?
sep_data.loc[0, ['channel', 'time', 'lane']]

# What happens when you type the code below?
sep_data.loc[[0, 10, 399], :]

NOTE: Labels must be found in the DataFrame or you will get a KeyError.

Indexing by labels loc differs from indexing by integers iloc. With loc, the both start bound and the stop bound are inclusive. When using loc, integers can be used, but the integers refer to the index label and not the position. For example, using loc and select 1:4 will get a different result than using iloc to select rows 1:4.

We can also select a specific data value using a row and column location within the DataFrame and iloc indexing:

# Syntax for iloc indexing to finding a specific data element
dat.iloc[row, column]

In this iloc example,

sep_data.iloc[2, 6]

gives the output

'ch=199'

Remember that Python indexing begins at 0. So, the index location [2, 6] selects the element that is 3 rows down and 7 columns over in the DataFrame.

Challenge - Range

1. What happens when you execute:

   • sep_data[0:1]
   • sep_data[:4]
   • sep_data[:-1]

2. What happens when you call:

   • sep_data.iloc[0:4, 1:4]
   • sep_data.loc[0:4, 1:4]

• How are the two commands different?

Subsetting Data using Criteria

We can also select a subset of our data using criteria. For example, we can select all rows that have a read length of over 1000:

sep_data[sep_data.length >= 1000]

Which produces a lot of output, so I’m not showing it.

Or we can select all rows that do not contain the year 2002:

sep_data[sep_data.length != 1000]

We can define sets of criteria too:

sep_data[(sep_data.length >= 1000) & (sep_data.length <= 4000)]

Python Syntax Cheat Sheet

Use can use the syntax below when querying data by criteria from a DataFrame. Experiment with selecting various subsets of the “surveys” data.

• Equals: ==
• Not equals: !=
• Greater than, less than: > or <
• Greater than or equal to >=
• Less than or equal to <=

Challenge - Queries

1. Select a subset of rows in the sep_data DataFrame that contain reads of length less than 1000, and more than 5000. How many rows did you end up with? What did your neighbor get?

2. You can use the isin command in Python to query a DataFrame based upon a list of values as follows:

   sep_data[sep_data['channel'].isin(['ch=233', 'ch=234'])]
   

Use the isin function to find all reads that contain particular lengths in the “sep_data” DataFrame. How many records contain these values?

1. Experiment with other queries. Create a query that finds all rows with a length value > or equal to 5000.

2. The ~ symbol in Python can be used to return the OPPOSITE of the selection that you specify in Python. It is equivalent to is not in. Write a query that selects all rows that did not come from channel 233 in the “sep_data” data.

Using masks to identify a specific condition

A mask can be useful to locate where a particular subset of values exist or don’t exist - for example, NaN, or “Not a Number” values. To understand masks, we also need to understand BOOLEAN objects in Python.

Boolean values include True or False. For example,

# Set x to 5
x = 5

# What does the code below return?
x > 5

# How about this?
x == 5

When we ask Python what the value of x > 5 is, we get False. This is because the condition,x is not greater than 5, is not met since x is equal to 5.

To create a boolean mask:

• Set the True / False criteria (e.g. values > 5 = True)
• Python will then assess each value in the object to determine whether the value meets the criteria (True) or not (False).
• Python creates an output object that is the same shape as the original object, but with a True or False value for each index location.

Let’s try this out. Let’s identify all locations in the survey data that have null (missing or NaN) data values. We can use the isnull method to do this. The isnull method will compare each cell with a null value. If an element has a null value, it will be assigned a value of True in the output object.

pd.isnull(sep_data)

A snippet of the output is below:

     length  sequence  quals  readName  runID  readNum  channel   time   lane
0     False     False  False     False  False    False    False  False  False
1     False     False  False     False  False    False    False  False  False
2     False     False  False     False  False    False    False  False  False
3     False     False  False     False  False    False    False  False  False
4     False     False  False     False  False    False    False  False  False
5     False     False  False     False  False    False    False  False  False
6     False     False  False     False  False    False    False  False  False
7     False     False  False     False  False    False    False  False  False
8     False     False  False     False  False    False    False  False  False
9     False     False  False     False  False    False    False  False  False
10    False     False  False     False  False    False    False  False  False
11    False     False  False     False  False    False    False  False  False
12    False     False  False     False  False    False    False  False  False
13    False     False  False     False  False    False    False  False  False
14    False     False  False     False  False    False    False  False  False
15    False     False  False     False  False    False    False  False  False
16    False     False  False     False  False    False    False  False  False

[395 rows x 9 columns]

To select the rows where there are null values, we can use the mask as an index to subset our data as follows:

# To select just the rows with NaN values, we can use the 'any()' method
sep_data[pd.isnull(sep_data).any(axis=1)]

Note that the weight column of our DataFrame contains many null or NaN values. We will explore ways of dealing with this in Lesson 20.

We can run isnull on a particular column too. What does the code below do?

# What does this do?
empty_lengths = sep_data[pd.isnull(sep_data['length'])]['length']
print(empty_lengths)

Let’s take a minute to look at the statement above. We are using the Boolean object pd.isnull(sep_data['length']) as an index to sep_data. We are asking Python to select rows that have a NaN value of weight.

Key Points