Indexing and Slicing

Estimated teaching time: 30 min

Estimated challenge time: 30 min

Key questions:

Learning objectives:

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

Indexing, Slicing and Subsetting

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

  • Indexing,
  • Slicing, and
  • Subsetting

Ensure the Pandas package is installed

!pip install pandas matplotlib
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[notice] A new release of pip is available: 24.1 -> 24.1.1
[notice] To update, run: pip install --upgrade pip

Loading our data

We will continue to use the surveys 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
surveys_df = pd.read_csv("surveys.csv")

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 species_id from the surveys_df DataFrame by name. There are two ways to do this:

# Method 1: select a 'subset' of the data using the column name
surveys_df['species_id'].head()
0    NL
1    NL
2    DM
3    DM
4    DM
Name: species_id, dtype: object
# Method 2: use the column name as an 'attribute'; gives the same output
surveys_df.species_id.head()
0    NL
1    NL
2    DM
3    DM
4    DM
Name: species_id, dtype: object

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

# Creates an object, surveys_species, that only contains the `species_id` column
surveys_species = surveys_df['species_id']

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
surveys_df[['species_id', 'site_id']].head()
species_id site_id
0 NL 2
1 NL 3
2 DM 2
3 DM 7
4 DM 3

What happens if you ask for a column that doesn’t exist?

surveys_df['speciess']

Outputs:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/Applications/anaconda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2392             try:
-> 2393                 return self._engine.get_loc(key)
   2394             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)()

KeyError: 'speciess'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-7-7d65fa0158b8> in <module>()
      1 
      2 # What happens if you ask for a column that doesn't exist?
----> 3 surveys_df['speciess']
      4 

/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2060             return self._getitem_multilevel(key)
   2061         else:
-> 2062             return self._getitem_column(key)
   2063 
   2064     def _getitem_column(self, key):

/Applications/anaconda/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
   2067         # get column
   2068         if self.columns.is_unique:
-> 2069             return self._get_item_cache(key)
   2070 
   2071         # duplicate columns & possible reduce dimensionality

/Applications/anaconda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
   1532         res = cache.get(item)
   1533         if res is None:
-> 1534             values = self._data.get(item)
   1535             res = self._box_item_values(item, values)
   1536             cache[item] = res

/Applications/anaconda/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
   3588 
   3589             if not isnull(item):
-> 3590                 loc = self.items.get_loc(item)
   3591             else:
   3592                 indexer = np.arange(len(self.items))[isnull(self.items)]

/Applications/anaconda/lib/python3.6/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2393                 return self._engine.get_loc(key)
   2394             except KeyError:
-> 2395                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2396 
   2397         indexer = self.get_indexer([key], method=method, tolerance=tolerance)

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)()

KeyError: 'speciess'

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

# What happens when you flip the order?
surveys_df[['site_id', 'species_id']].head()
site_id species_id
0 2 NL
1 3 NL
2 2 DM
3 7 DM
4 3 DM

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 0. 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]

indexing diagram slicing diagram

Challenge - Extracting data

  1. What value does the code a[0] return?

  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)] ?

Solutions - Extracting data

a[0]
1
# Solution #2
# a[5]

Solution #2

In above example, the error says list index out of range. This means we don’t have index 5 in our list. The maximum index for a is 4, as indexing starts at 0.

# Solution #3
# a[len(a)]

Solution - # 4 - Extracting data

This also gives an error, because len(a) is 5 which is not the maximum index

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 with our surveys data:

# Select rows 0, 1, 2 (row 3 is not selected)
surveys_df[0:3]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
1 2 7 16 1977 3 NL M 33.0 NaN
2 3 7 16 1977 2 DM F 37.0 NaN

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

Now lets select the first 5 rows (rows 0, 1, 2, 3, 4).

surveys_df[:5]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
1 2 7 16 1977 3 NL M 33.0 NaN
2 3 7 16 1977 2 DM F 37.0 NaN
3 4 7 16 1977 7 DM M 36.0 NaN
4 5 7 16 1977 3 DM M 35.0 NaN 
# Select the last element in the list
# (the slice starts at the last element, and ends at the end of the list)
surveys_df[-1:]
record_id month day year site_id species_id sex hindfoot_length weight
35548 35549 12 31 2002 5 NaN NaN NaN NaN

We can also reassign values within subsets of our DataFrame.

Let’s create a brand new clean dataframe from the original data CSV file.

surveys_df = pd.read_csv("surveys.csv")

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]

surveys_df.iloc[0:3, 1:4]
month day year
0 7 16 1977
1 7 16 1977
2 7 16 1977

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
surveys_df.loc[[0, 10], :]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
10 11 7 16 1977 5 DS F 53.0 NaN 
# What does this do?
surveys_df.loc[0, ['species_id', 'site_id', 'weight']]
species_id     NL
site_id         2
weight        NaN
Name: 0, dtype: object
# What happens when you uncomment and run the code below?
# surveys_df.loc[[0, 10, 35549], :]

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 following iloc example:

surveys_df.iloc[2, 6]
'F'

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:

    - `surveys_df[0:1]`
- `surveys_df[:4]`
- `surveys_df[:-1]`

  2. What happens when you call:

    - `surveys_df.iloc[0:4, 1:4]`

Solution - Range

# Solution - Range - #1 (a)
surveys_df[0:1]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN 
# Solution - Range - #1 (b)
surveys_df[:4]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
1 2 7 16 1977 3 NL M 33.0 NaN
2 3 7 16 1977 2 DM F 37.0 NaN
3 4 7 16 1977 7 DM M 36.0 NaN 
# Solution - Range - #1 (c)
surveys_df[:-1]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
1 2 7 16 1977 3 NL M 33.0 NaN
2 3 7 16 1977 2 DM F 37.0 NaN
3 4 7 16 1977 7 DM M 36.0 NaN
4 5 7 16 1977 3 DM M 35.0 NaN
... ... ... ... ... ... ... ... ... ...
35543 35544 12 31 2002 15 US NaN NaN NaN
35544 35545 12 31 2002 15 AH NaN NaN NaN
35545 35546 12 31 2002 15 AH NaN NaN NaN
35546 35547 12 31 2002 10 RM F 15.0 14.0
35547 35548 12 31 2002 7 DO M 36.0 51.0

35548 rows × 9 columns

# Solution - Range - #2
surveys_df.iloc[0:4, 1:4]
month day year
0 7 16 1977
1 7 16 1977
2 7 16 1977
3 7 16 1977

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 year value of 2002:

surveys_df[surveys_df.year == 2002].head()
record_id month day year site_id species_id sex hindfoot_length weight
33320 33321 1 12 2002 1 DM M 38.0 44.0
33321 33322 1 12 2002 1 DO M 37.0 58.0
33322 33323 1 12 2002 1 PB M 28.0 45.0
33323 33324 1 12 2002 1 AB NaN NaN NaN
33324 33325 1 12 2002 1 DO M 35.0 29.0

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

surveys_df[surveys_df.year != 2002]
record_id month day year site_id species_id sex hindfoot_length weight
0 1 7 16 1977 2 NL M 32.0 NaN
1 2 7 16 1977 3 NL M 33.0 NaN
2 3 7 16 1977 2 DM F 37.0 NaN
3 4 7 16 1977 7 DM M 36.0 NaN
4 5 7 16 1977 3 DM M 35.0 NaN
... ... ... ... ... ... ... ... ... ...
33315 33316 12 16 2001 11 NaN NaN NaN NaN
33316 33317 12 16 2001 13 NaN NaN NaN NaN
33317 33318 12 16 2001 14 NaN NaN NaN NaN
33318 33319 12 16 2001 15 NaN NaN NaN NaN
33319 33320 12 16 2001 16 NaN NaN NaN NaN

33320 rows × 9 columns

We can define sets of criteria too:

surveys_df[(surveys_df.year >= 1980) & (surveys_df.year <= 1985)]
record_id month day year site_id species_id sex hindfoot_length weight
2270 2271 1 15 1980 8 DO M 35.0 53.0
2271 2272 1 15 1980 11 PF F 16.0 10.0
2272 2273 1 15 1980 18 DM F 34.0 33.0
2273 2274 1 15 1980 11 DM M 38.0 37.0
2274 2275 1 15 1980 8 DO F 33.0 29.0
... ... ... ... ... ... ... ... ... ...
11222 11223 12 8 1985 4 DM M 36.0 40.0
11223 11224 12 8 1985 11 DM M 37.0 49.0
11224 11225 12 8 1985 7 PE M 20.0 18.0
11225 11226 12 8 1985 1 DM M 38.0 47.0
11226 11227 12 8 1985 15 NaN NaN NaN NaN

8957 rows × 9 columns

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 surveys_df DataFrame that contain data from the year 1999 and that contain weight values less than or equal to 8. How many rows did you end up with? What did your neighbor get?

  2. (Extra) Use the isin function to find all plots that contain PB and PL species in the “surveys” DataFrame. How many records contain these values?

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

surveys_df[surveys_df['species_id'].isin([listGoesHere])]

Solution - Queries

## Solution - Queries #1
surveys_df[(surveys_df["year"] == 1999) & (surveys_df["weight"] <= 8)]
record_id month day year site_id species_id sex hindfoot_length weight
29082 29083 1 16 1999 21 RM M 16.0 8.0
29196 29197 2 20 1999 18 RM M 18.0 8.0
29421 29422 3 15 1999 16 RM M 15.0 8.0
29903 29904 10 10 1999 4 PP M 20.0 7.0
29905 29906 10 10 1999 4 PP M 21.0 4.0 
# when only interested in how many, the sum of True values could be used as well: 
sum((surveys_df["year"] == 1999) & (surveys_df["weight"] <= 8))
5
# Solution - Queries #2
surveys_df[surveys_df['species_id'].isin(['PB', 'PL'])]['site_id'].unique()
array([ 1, 10,  6, 24,  2, 23, 19, 12, 20, 22,  3,  9, 14, 13, 21,  7, 11,
       15,  4, 16, 17,  8, 18,  5])
# To get number of records
surveys_df[surveys_df['species_id'].isin(['PB', 'PL'])].shape
(2927, 9)

Extra Challenges

  1. (Extra) Create a query that finds all rows with a weight value greater than (>) or equal to 0.

  2. (Extra) 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 with sex NOT equal to ‘M’ or ‘F’ in the “surveys” data.

sum(surveys_df["weight"]>=0)
32283
surveys_df[~surveys_df["sex"].isin(['M', 'F'])]
record_id month day year site_id species_id sex hindfoot_length weight
13 14 7 16 1977 8 DM NaN NaN NaN
18 19 7 16 1977 4 PF NaN NaN NaN
33 34 7 17 1977 17 DM NaN NaN NaN
56 57 7 18 1977 22 DM NaN NaN NaN
76 77 8 19 1977 4 SS NaN NaN NaN
... ... ... ... ... ... ... ... ... ...
35527 35528 12 31 2002 13 US NaN NaN NaN
35543 35544 12 31 2002 15 US NaN NaN NaN
35544 35545 12 31 2002 15 AH NaN NaN NaN
35545 35546 12 31 2002 15 AH NaN NaN NaN
35548 35549 12 31 2002 5 NaN NaN NaN NaN

2511 rows × 9 columns

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
False
# How about this?
x == 5
True

Extra Challenges - Putting it all together

  1. Create a new DataFrame that only contains observations with sex values that are not female or male. Assign each sex value in the new DataFrame to a new value of ‘x’. Determine the number of null values in the subset.

  2. Create a new DataFrame that contains only observations that are of sex male or female and where weight values are greater than 0. Create a stacked bar plot of average weight by plot with male vs female values stacked for each plot.

  3. Count the number of missing values per column. Hint: The method .count() gives you the number of non-NA observations per column.

Solution Extra Challenges

# Solution extra challenge 1
new = surveys_df[~surveys_df['sex'].isin(['M', 'F'])].copy()
new['sex']='x'
print(len(new))
2511
# We can verify the number of NaN values with
sum(surveys_df['sex'].isnull())
2511
# Solution extra challenge 2

# selection of the data with isin
stack_selection = surveys_df[(surveys_df['sex'].isin(['M', 'F'])) &
                            surveys_df["weight"] > 0.][["sex", "weight", "site_id"]]

# calculate the mean weight for each site id and sex combination:
stack_selection = stack_selection.groupby(["site_id", "sex"]).mean().unstack()

# Plot inside jupyter notebook
%matplotlib inline

# and we can make a stacked bar plot from this:
stack_selection.plot(kind='bar', stacked=True)