Data Frames

Last updated on 2022-12-06 | Edit this page

Overview

Questions

How to use dataframes in Python?

Objectives

Import data set as dataframe
Inspect data frame and access data
Produce an overview of data features
Create data plots using matplotlib

Video Tutorials

Pandas Dataframes - Import and Data Handling

Pandas Dataframes - Visualisation

Introduction

This is our first lesson on learning and understanding dataframes in Python.

Challenge: The diabetes data set

Here is a screenshot of the so-called diabetes data set. It is taken from this webpage and it is one of the example data sets used to illustrate machine learning functionality in scikit-learn (Part III and Part IV of the course).

This figure captures only the top part of the data. On the webpage you need to scroll down considerably to view the whole content. Thus, to get an overview of the dataset is the first main task in Data Science.

The lesson

introduces code to read and inspect the data
works with a specific data frame and extracts some techniques to get an overview
discusses the concept ‘distribution’ as a way of summarising data in a single figure

To get to know a dataset you need to

access the data
check the content
produce a summary of basic properties

In this lesson we will only look at univariate features where each data column is studied independently of the others. Further properties and bivariate features will be the topic of the next lesson.

Work Through Example

Reading data into a Pandas DataFrame

The small practice data file for this section is called ‘everleys_data.csv’ and can be downloaded using the link given above in “Materials for this Lesson”. To start, please create a subfolder called ‘data’ in the current directory and put the data file in it. It can now be accessed using the relative path data/everleys_data.csv or data\everleys_data.csv, respectively.

The file everleys_data.csv contains blood concentrations of calcium and sodium ions from 17 patients with Everley’s syndrome. The data are taken from a BMJ statistics tutorial. The data are stored as comma-separated values (csv), two values for each patient.

To get to know a dataset, we will use the Pandas package and the Matplotlib plotting. The Pandas package for data science is included in the Anaconda distribution of Python. Check this link for installation instructions to get started.

If you are not using the Anaconda distribution, pease refer to these guidelines.

To use the functions contained in Pandas they need to be imported. Our dataset is in ‘.csv’ format, and we therefore need to read it from a csv file. For this, we import the function read_csv. This will create a Pandas dataframe.

PYTHON

import sys
print(sys.version)

OUTPUT

3.10.8 (main, Oct 18 2022, 06:43:21) [GCC 9.4.0]

PYTHON

from pandas import read_csv

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'pandas'

Executing this code does not lead to any output on the screen. However, the function is now ready to be used. To use it, we type its name and provide the required arguments. The following code should import the Everley’s data into your JupyterLab notebook (or other Python environment):

PYTHON

# for Mac OSX and Linux
# (please go to the next cell if using Windows)

df = read_csv("data/everleys_data.csv")

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'read_csv' is not defined

PYTHON

# please uncomment for Windows
# (please go to previous cell if using Mac OSX or Linux)

# df = read_csv("data\everleys_data.csv")

This code uses the read_csv function from Pandas to read data from a data file, in this case a file with extension ‘.csv’. Note that the location of the data file is specified within quotes by the relative path to the subfolder ‘data’ followed by the file name. Use the JupyterLab file browser to check that subfolder exists and has the file in it.

After execution of the code, the data are contained in a variable called df. This is a structure referred to as a Pandas DataFrame.

A Pandas dataframe is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it as a spreadsheet.

To see the contents of df, simply use:

PYTHON

df

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

(Compare with the result of print(df) which displays the contents in a different format.)

The output shows in the first column an index, integers from 0 to 17; and the calcium and sodium concentrations in columns 2 and 3, respectively. The default indexing starts from zero (Python is a ‘zero-based’ programming language).

In a dataframe, the first column is referred to as Indices, the first row is referred to as Labels. Note that the row with the labels is excluded from the row count. Similarly, the row with the indices is excluded from the column count.

For large data sets, the function head is a convenient way to get a feel of the dataset.

PYTHON

df.head()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Without any input argument, this displays the first five data lines of the dataframe. You can specify alter the number of rows displayed by including a single integer as argument, e.g. head(10).

If you feel there are too many decimal places in the default view, you can restrict their number by using the round function:

PYTHON

df.head().round(2)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

While we can see how many rows there are in a dataframe when we display the whole data frame and look at the last index, there is a convenient way to obtain the number directly:

PYTHON

no_rows = len(df)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print('Data frame has', no_rows, 'rows')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'no_rows' is not defined

You could see above, that the columns of the dataframe have labels. To see all labels:

PYTHON

column_labels = df.columns

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print(column_labels)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'column_labels' is not defined

Now we can count the labels to obtain the number of columns:

PYTHON

no_columns = len(column_labels)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'column_labels' is not defined

PYTHON

print('Data frame has', no_columns, 'columns')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'no_columns' is not defined

And if you want to have both the number of the rows and the columns together, use shape. Shape returns a tuple of two numbers, first the number of rows, then the number of columns.

PYTHON

df_shape = df.shape

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print('Data frame has', df_shape[0], 'rows and',df_shape[1],  'columns')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_shape' is not defined

Notice that shape (like columns) is not followed by round parenthesis. It is not a function that can take arguments. Technically, shape is a ‘property’ of the dataframe.

To find out what data type is contained in each of the columns, us dtypes, another ‘property’:

PYTHON

df.dtypes

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

In this case, both columns contain floating point (decimal) numbers.

DIY1: Read data into a dataframe

Download the data file ‘loan_data.csv’ using the link given above in “Materials for this Lesson”. It contains data that can be used for the assessment of loan applications. Read the data into a DataFrame. It is best to assign it a name other than ‘df’ (to avoid overwriting the Evereley data set).

Display the first ten rows of the Loan data set to see its contents. It is taken from a tutorial on Data Handling in Python which you might find useful for further practice.

From this exercise we can see that a dataframe can contain different types of data: real numbers (e.g. LoanAmount), integers (ApplicantIncome), categorical data (Gender), and strings (Loan_ID).

Show me the solution

PYTHON

from pandas import read_csv

# dataframe from .csv file

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'pandas'

PYTHON

df_loan = read_csv("data/loan_data.csv") 

# display contents

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'read_csv' is not defined

PYTHON

df_loan.head(10)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_loan' is not defined

Accessing data in a DataFrame

If a datafile is large and you only want to check the format of data in a specific column, you can limit the display to that column. To access data contained in a specific column of a dataframe, we can use a similar convention as in a Python dictionary, treating the column names as ‘keys’. E.g. to show all rows in column ‘Calcium’, use:

PYTHON

df['calcium']

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

To access individual rows of a column we use two pairs of square brackets:

PYTHON

df['calcium'][0:3]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Here all rules for slicing can be applied. As for lists and tuples, the indexing of rows is semi-inclusive, lower boundary included, upper boundary excluded. Note that the first pair of square brackets refers to a column and the second pair refers to the rows. This is different from e.g. accessing items in a nested list.

Accessing items in a Pandas dataframe is analogous to accessing the values in a Python dictionary by referring to its keys.

To access non-contiguous elements, we use an additional pair of square brackets (as if for a list within a list):

PYTHON

df['calcium'][[1, 3, 7]]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Another possibility to index and slice a dataframe is the use of the ‘index location’ or iloc property. It refers first to rows and then to columns by index, all within a single pair of brackets. For example, to get all rows : of the first column (index 0), you use:

PYTHON

df.iloc[:, 0]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

To display only the first three calcium concentrations, you use slicing, remembering that the upper bound is excluded):

PYTHON

df.iloc[0:3, 0]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

To access non-consecutive values, we can use a pair of square brackets within the pair of square brackets:

PYTHON

df.iloc[[2, 4, 7], 0]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Similarly, we can access the values from multiple columns:

PYTHON

df.iloc[[2, 4, 7], :]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

To pick only the even rows from the two columns, check this colon notation:

PYTHON

df.iloc[:18:2, :]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

The number after the second colon indicates the stepsize.

DIY2: Select data from dataframe

Display the calcium and sodium concentrations of all patients except the first. Check the model solution at the bottom for options.

Show me the solution

PYTHON

df[['calcium', 'sodium']][1:]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Mixing the ways to access specific data in a dataframe can be confusing and needs practice.

Search for missing values

Some tables contain missing entries. You can check a dataframe for such missing entries. If no missing entry is found, the function isnull will return False.

PYTHON

df.isnull().any()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

This shows that there are no missing entries in our dataframe.

DIY3: Find NaN in dataframe

In the Loan data set, check the entry ‘Self-employed’ for ID LP001059. It shows how a missing value is represented as ‘NaN’ (not a number).

Verify that the output of isnull in this case is True

Show me the solution

PYTHON

df_loan['Self_Employed'][8]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_loan' is not defined

PYTHON

df_loan['Self_Employed'][8:9].isnull()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_loan' is not defined

Basic data features

Summary Statistics

To get a summary of basic data features use the function describe:

PYTHON

description = df.describe()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

description

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'description' is not defined

The describe function produces a new dataframe (here called ‘descrition’) that contains the number of samples, the mean, the standard deviation, minimum, 25th, 50th, 75th percentile, and the maximum value for each column of the data. Note that the indices of the rows have now been replaced by strings. To access rows, it is possible to refer to those names using the loc property. E.g. to access the mean of the calcium concentrations from the description, each of the following is valid:

PYTHON

# Option 1
description.loc['mean']['calcium']

# Option 2

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'description' is not defined

PYTHON

description.loc['mean'][0]

# Option 3

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'description' is not defined

PYTHON

description['calcium']['mean']

# Option 4

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'description' is not defined

PYTHON

description['calcium'][1]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'description' is not defined

DIY4: Practice

Use your own .csv data set to practice. (If you don’t have a data set at hand, any excel table can be exported as .csv.) Read it into a dataframe, check its header, access indivdual values or sets of values. Create a statistics using describe and check for missing values using .isnull.

Show me the solution

[ad libitum]

Iterating through the columns

Now we know how to access all data in a dataframe and how to get a summary statistics over each column.

Here is code to iterate through the columns and access the first two concentrations:

PYTHON

for col in df:
    
    print(df[col][0:2])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

As a slightly more complex example, we access the median (‘50%’) of each column in the description and add it to a list:

PYTHON

conc_medians = list()

for col in df:
    
    conc_medians.append(df[col].describe()['50%'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print('The columns are: ', list(df.columns))

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print('The medians are: ', conc_medians)

OUTPUT

The medians are:  []

This approach is useful for data frames with a large number of columns. For instance, it is possible to then create a boxplot or histogram for the means, medians etc. of the dataframe and thus to get an overview of all (comparable) columns.

Selecting a subset based on a template

An analysis of a data set may need to be done on part of the data. This can often be formulated by using a logical condition which specifies the required subset.

For this we will assume that some of the data are labelled ‘0’ and some are labelled ‘1’. Let us therefore see how to add a new column to our Evereleys data frame which contains the (in this case arbitrary) labels.

First we randomly create as many labels as we have rows in the data frame. We can use the randint function which we import from ‘numpy.random’. randint in its simple form takes two arguments. First the upper bound of the integer needed, where by default it starts from zero. As Python is exclusive on the upper bound, providing ‘2’ will thus yield either ‘0’ or ‘1’ only.

PYTHON

from numpy.random import randint

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'numpy'

PYTHON

no_rows = len(df)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

randomLabel = randint(2, size=no_rows)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randint' is not defined

PYTHON

print('Number of rows:  ', no_rows)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'no_rows' is not defined

PYTHON

print('Number of Labels:', len(randomLabel))

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randomLabel' is not defined

PYTHON

print('Labels:          ', randomLabel)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randomLabel' is not defined

Note how we obtain the number of rows (18) using len and do not put it directly into the code.

Now we create a new data column in our df dataframe which contains the labels. To create a new column, you can simply refer to a column name that does not yet exist and assign values to it. Let us call it ‘gender’, assuming that ‘0’ represents male and ‘1’ represents female.

As gender specification can include more than two labels, try to create a column with more than two randomly assigned labels e.g. (0, 1, 2).

PYTHON

df['gender'] = randomLabel

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randomLabel' is not defined

PYTHON

df.head()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Now we can use the information contained in ‘gender’ to filter the data by gender. To achieve this, we use a conditional statement. Let us check which of the rows are labelled as ‘1’:

PYTHON

df['gender'] == 1

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

If we assign the result of the conditional statement (Boolean True or False) to a variable, then this variable can act as a template to filter the data. If we call the data frame with that variable, we will only get the rows where the condition was found to be True:

PYTHON

df_female = df['gender'] == 1

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

df[df_female]

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

Using the Boolean, we only pick the rows that are labelled ‘1’ and thus get a subset of the data according to the label.

DIY5: Using a template

Modify the code to calculate the number of samples labelled 0 and check the number of rows of that subset.

Show me the solution

PYTHON

from numpy.random import randint

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'numpy'

PYTHON

no_rows = len(df)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

randomLabel = randint(2, size=no_rows)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randint' is not defined

PYTHON

df['gender'] = randomLabel

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'randomLabel' is not defined

PYTHON

df_male = df['gender'] == 0

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

no_males = len(df[df_male])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined

PYTHON

print(no_males, 'samples are labelled "male".')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'no_males' is not defined

Visualisation of data

It is easy to see from the numbers that the concentrations of sodium are much higher than that of calcium. However, to also compare the medians, percentiles and the spread of the data it is better to use visualisation.

The simplest way of visualisation is to use Pandas functionality which offers direct ways of plotting. Here is an example where a boxplot is created for each column:

PYTHON

import matplotlib.pyplot as plt

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'matplotlib'

PYTHON

df = read_csv("data/everleys_data.csv")

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'read_csv' is not defined

PYTHON

plt.boxplot(df)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'plt' is not defined

PYTHON

plt.show()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'plt' is not defined

By default, boxplots are shown for all columns if no further argument is given to the function (empty round parenthesis). As the calcium plot is rather squeezed we may wish to see it individually. This can be done by specifying the calcium column as an argument:

PYTHON


# Boxplot of calcium results
# df.boxplot(column='calcium');


plt.boxplot(df['calcium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'plt' is not defined

PYTHON

plt.show()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'plt' is not defined

Plots using Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

The above is an easy way to create boxplots directly on the dataframe. It is based on the library Matplotlib and specifically uses the pyplot library. For simplicity, the code is put in a convenient Pandas function.

However, we are going to use Matplotlib extensively later on in the course, and we therefore now introduce the direct, generic way of using it.

For this, we import the function subplots from the pyplot library:

PYTHON

from matplotlib.pyplot import subplots

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'matplotlib'

The way to use subplots is to first set up a figure environment (below it is called ‘fig’) and an empty coordinate system (below called ‘ax’). The plot is then done using one of the many methods available in Matplotlib. We apply it to the coordinate system ‘ax’.

As an example, let us create a boxplot of the calcium variable. As an argument of the function we need to specify the data. We can use the values of the ‘calcium’ concentrations from the column with that name:

PYTHON

fig, ax = subplots()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax.boxplot(df['calcium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax.set_title('Boxplot of Everley\'s Calcium');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

Note how following the actual plot we define the title of the plot by referring to the same coordinate system ax.

The value of subplots becomes apparent when we try to create more than one plot in a single figure.

Here is an example to create two boxplots next to each other. The keyword arguments to use is ‘ncols’ which is the number of figures per row. ‘ncols=2’ indicates that you want to have two plots next to each other.

PYTHON

fig, ax = subplots(ncols=2)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax[0].boxplot(df['calcium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[0].set_title('Calcium')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].boxplot(df['sodium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].set_title('Sodium');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

Note that you now have to refer to each of the subplots by indexing the coordinate system ‘ax’. | This figure gives a good overview of the Everley’s data.

If you prefer to have the boxplots of both columns in a single figure, that can also be done:

PYTHON

fig, ax = subplots(ncols=1, nrows=1)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax.boxplot([df['calcium'], df['sodium']], positions=[1, 2])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax.set_title('Boxplot of Calcium (left) and Sodium (right)')

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

DIY6: Boxplot from Loan data

Plot the boxplots of the ‘ApplicantIncome’ and the ‘CoapplicantIncome’ in the Loan data using the above code.

Show me the solution

PYTHON

fig, ax = subplots(ncols=1, nrows=1)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax.boxplot([df_loan['ApplicantIncome'], df_loan['CoapplicantIncome']], positions=[1, 2])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax.set_title('Applicant Income (left) & Co-Applicant Income (right)');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

Applicant Income (left) & Co-Applicant Income (right)

Histogram

Another good overview is the histogram: Containers or ‘bins’ are created over the range of values found within a column and the count of the values for each bin is plotted on the vertical axis.

PYTHON

fig, ax = subplots(ncols=2, nrows=1)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax[0].hist(df['calcium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[0].set(xlabel='Calcium', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].hist(df['sodium'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].set(xlabel='Sodium', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

fig.suptitle('Histograms of Everley concentrations', fontsize=15);

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'fig' is not defined

This also shows how to add labels to the axes and a title to the overall figure.

This uses the default value for the generation of the bins. It is set to 10 bins over the range of which values are found. The number of bins in the histogram can be changed using the keyword argument ‘bins’:

PYTHON

fig, ax = subplots(ncols=2, nrows=1)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax[0].hist(df['calcium'], bins=5)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[0].set(xlabel='Calcium, 5 bins', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].hist(df['calcium'], bins=15)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].set(xlabel='Calcium, 15 bins', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

fig.suptitle('Histograms with Different Binnings', fontsize=16);

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'fig' is not defined

Note how the y-label of the right figure is not placed well. To correct for the placement of the labels and the title, you can use tight_layout on the figure:

PYTHON

fig, ax = subplots(ncols=2, nrows=1)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax[0].hist(df['calcium'], bins=5)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[0].set(xlabel='Calcium, 5 bins', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].hist(df['calcium'], bins=15)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax[1].set(xlabel='Calcium, 15 bins', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

fig.suptitle('Histograms with Different Binnings', fontsize=16);

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'fig' is not defined

PYTHON

fig.tight_layout()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'fig' is not defined

DIY7: Create the histogram of a column

Take the loan data and display the histogram of the loan amount that people asked for. (Loan amounts are divided by 1000, i.e. in k£ on the horizontal axis). Use e.g. 20 bins.

Show me the solution

PYTHON

# Histogram of loan amounts in k£

fig, ax = subplots()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

ax.hist(df_loan['LoanAmount'], bins=20)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax.set(xlabel='Loan amount', ylabel='Count');

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

PYTHON

ax.set_title('Histograms of Loan Amounts', fontsize=16);

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

Handling the Diabetes Data Set

We now return to the data set that started our enquiry into the handling of data in a dataframe.

We will now:

Import the diabetes data from ‘sklearn’
Check the shape of the dataframe and search for NANs
Get a summary plot of one of its statistical quantities (e.g. mean) for all columns

First we import the data set and check its head. Wait until the numbers show below the code, it might take a while.

PYTHON

from sklearn import datasets

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'sklearn'

PYTHON

diabetes = datasets.load_diabetes()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'datasets' is not defined

PYTHON

X = diabetes.data

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'diabetes' is not defined

PYTHON

from pandas import DataFrame

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'pandas'

PYTHON

df_diabetes = DataFrame(data=X)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'DataFrame' is not defined

PYTHON

df_diabetes.head()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_diabetes' is not defined

If you don’t see all columns, use the cursor to scroll to the right.

Now let’s check the number of columns and rows.

PYTHON

no_rows = len(df_diabetes)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_diabetes' is not defined

PYTHON

no_cols = len(df_diabetes.columns)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_diabetes' is not defined

PYTHON

print('Rows:', no_rows, 'Columns:', no_cols)

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'no_rows' is not defined

There are 442 rows organised in 10 columns.

To get an overview, let us extract the mean of each column using ‘describe’ and plot all means as a bar chart. The Matplotlib function to plot a bar chart is bar:

PYTHON

conc_means = list()

for col in df_diabetes:
    conc_means.append(df_diabetes[col].describe()['mean'])

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_diabetes' is not defined

PYTHON

print('The columns are: ', list(df_diabetes.columns))

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df_diabetes' is not defined

PYTHON

print('The medians are: ', conc_means, 2)

OUTPUT

The medians are:  [] 2

PYTHON

fig, ax = subplots()

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'subplots' is not defined

PYTHON

bins = range(10)

ax.bar(bins, conc_means);

ERROR

Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'ax' is not defined

The bars in this plot go up and down. Note, however, that the vertical axis has values ranging from -10^(-15) to +10^(-15). This means that for all practical purposes all means are zero. This is not a coincidence. The original values have been normalised to zero mean for the purpose of applying some machine learning algorithm to them.

In this example, we see how important it is to check the data before working with them.

Assignment: The cervical cancer data set

Download the cervical cancer data set provided, import it using read_csv.

How many rows and columns are there?
How many columns contain floating point numbers (float64)?
How many of the subjects are smokers?
Calculate the percentage of smokers
Plot the age distribution (with e.g. 50 bins)
Get the mean and standard distribution of age of first sexual intercourse

Keypoints

learned importing data set as dataframe
learned inspecting data frame and accessing data
Producing an overview of data features
Creating data plots using matplotlib

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