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Understand Your Machine Learning Data With Descriptive Statistics in Python

You must understand your data in order to get the best results.

In this post you will discover 7 recipes that you can use in Python to learn more about your machine learning data.

Let’s get started.

Understand Your Machine Learning Data With Descriptive Statistics in Python

Understand Your Machine Learning Data With Descriptive Statistics in Python

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Python Recipes To Understand Your Machine Learning Data

This section lists 7 recipes that you can use to better understand your machine learning data.

Each recipe is demonstrated by loading the Pima Indians Diabetes classification dataset from the UCI Machine Learning repository.

Open your python interactive environment and try each recipe out in turn.

1. Peek at Your Data

There is no substitute for looking at the raw data.

Looking at the raw data can reveal insights that you cannot get any other way. It can also plant seeds that may later grow into ideas on how to better preprocess and handle the data for machine learning tasks.

You can review the first 20 rows of your data using the head() function on the Pandas DataFrame.

# View first 20 rows importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) peek = data.head(20) print(peek) 

You can see that the first column lists the row number, which is handy for referencing a specific observation.

    preg  plas  pres  skin  test  mass   pedi  age  class 0      6   148    72    35     0  33.6  0.627   50      1 1      1    85    66    29     0  26.6  0.351   31      0 2      8   183    64     0     0  23.3  0.672   32      1 3      1    89    66    23    94  28.1  0.167   21      0 4      0   137    40    35   168  43.1  2.288   33      1 5      5   116    74     0     0  25.6  0.201   30      0 6      3    78    50    32    88  31.0  0.248   26      1 7     10   115     0     0     0  35.3  0.134   29      0 8      2   197    70    45   543  30.5  0.158   53      1 9      8   125    96     0     0   0.0  0.232   54      1 10     4   110    92     0     0  37.6  0.191   30      0 11    10   168    74     0     0  38.0  0.537   34      1 12    10   139    80     0     0  27.1  1.441   57      0 13     1   189    60    23   846  30.1  0.398   59      1 14     5   166    72    19   175  25.8  0.587   51      1 15     7   100     0     0     0  30.0  0.484   32      1 16     0   118    84    47   230  45.8  0.551   31      1 17     7   107    74     0     0  29.6  0.254   31      1 18     1   103    30    38    83  43.3  0.183   33      0 19     1   115    70    30    96  34.6  0.529   32      1 

2. Dimensions of Your Data

You must have a very good handle on how much data you have, both in terms of rows and columns.

  • Too many rows and algorithms may take too long to train. Too few and perhaps you do not have enough data to train the algorithms.
  • Too many features and some algorithms can be distracted or suffer poor performance due to the curse of dimensionality.

You can review the shape and size of your dataset by printing the shape property on the Pandas DataFrame.

# Dimensions of your data importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) shape = data.shape print(shape) 

The results are listed in rows then columns. You can see that the dataset has 768 rows and 9 columns.

(768, 9) 

3. Data Type For Each Attribute

The type of each attribute is important.

Strings may need to be converted to floating point values or integers to represent categorical or ordinal values.

You can get an idea of the types of attributes by peeking at the raw data, as above. You can also list the data types used by the DataFrame to characterize each attribute using the dtypes property.

# Data Types for Each Attribute importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) types = data.dtypes print(types) 

You can see that most of the attributes are integers and that mass and pedi are floating point values.

preg       int64 plas       int64 pres       int64 skin       int64 test       int64 mass     float64 pedi     float64 age        int64 class      int64 dtype: object 

4. Descriptive Statistics

Descriptive statistics can give you great insight into the shape of each attribute.

Often you can create more summaries than you have time to review. The describe() function on the Pandas DataFrame lists 8 statistical properties of each attribute:

  • Count
  • Mean
  • Standard Devaition
  • Minimum Value
  • 25th Percentile
  • 50th Percentile (Median)
  • 75th Percentile
  • Maximum Value
# Statistical Summary importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) pandas.set_option('display.width', 100) pandas.set_option('precision', 3) description = data.describe() print(description) 

You can see that you do get a lot of data. You will note some calls to pandas.set_option() in the recipe to change the precision of the numbers and the preferred width of the output. This is to make it more readable for this example.

When describing your data this way, it is worth taking some time and reviewing observations from the results. This might include the presence of “ NA ” values for missing data or surprising distributions for attributes.

          preg     plas     pres     skin     test     mass     pedi      age    class count  768.000  768.000  768.000  768.000  768.000  768.000  768.000  768.000  768.000 mean     3.845  120.895   69.105   20.536   79.799   31.993    0.472   33.241    0.349 std      3.370   31.973   19.356   15.952  115.244    7.884    0.331   11.760    0.477 min      0.000    0.000    0.000    0.000    0.000    0.000    0.078   21.000    0.000 25%      1.000   99.000   62.000    0.000    0.000   27.300    0.244   24.000    0.000 50%      3.000  117.000   72.000   23.000   30.500   32.000    0.372   29.000    0.000 75%      6.000  140.250   80.000   32.000  127.250   36.600    0.626   41.000    1.000 max     17.000  199.000  122.000   99.000  846.000   67.100    2.420   81.000    1.000 

5. Class Distribution (Classification Only)

On classification problems you need to know how balanced the class values are.

Highly imbalanced problems (a lot more observations for one class than another) are common and may need special handling in the data preparation stage of your project.

You can quickly get an idea of the distribution of the class attribute in Pandas.

# Class Distribution importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) class_counts = data.groupby('class').size() print(class_counts) 

You can see that there are nearly double the number of observations with class 0 (no onset of diabetes) than there are with class 1 (onset of diabetes).

class 0    500 1    268 

6. Correlation Between Attributes

Correlation refers to the relationship between two variables and how they may or may not change together.

The most common method for calculating correlation is Pearson’s Correlation Coefficient , that assumes a normal distribution of the attributes involved. A correlation of -1 or 1 shows a full negative or positive correlation respectively. Whereas a value of 0 shows no correlation at all.

Some machine learning algorithms like linear and logistic regression can suffer poor performance if there are highly correlated attributes in your dataset. As such, it is a good idea to review all of the pair-wise correlations of the attributes in your dataset. You can use the corr() function on the Pandas DataFrame to calculate a correlation matrix.

# Pairwise Pearson correlations importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) pandas.set_option('display.width', 100) pandas.set_option('precision', 3) correlations = data.corr(method='pearson') print(correlations) 

The matrix lists all attributes across the top and down the side, to give correlation between all pairs of attributes (twice, because the matrix is symmetrical). You can see the diagonal line through the matrix from the top left to bottom right corners of the matrix shows perfect correlation of each attribute with itself.

        preg   plas   pres   skin   test   mass   pedi    age  class preg   1.000  0.129  0.141 -0.082 -0.074  0.018 -0.034  0.544  0.222 plas   0.129  1.000  0.153  0.057  0.331  0.221  0.137  0.264  0.467 pres   0.141  0.153  1.000  0.207  0.089  0.282  0.041  0.240  0.065 skin  -0.082  0.057  0.207  1.000  0.437  0.393  0.184 -0.114  0.075 test  -0.074  0.331  0.089  0.437  1.000  0.198  0.185 -0.042  0.131 mass   0.018  0.221  0.282  0.393  0.198  1.000  0.141  0.036  0.293 pedi  -0.034  0.137  0.041  0.184  0.185  0.141  1.000  0.034  0.174 age    0.544  0.264  0.240 -0.114 -0.042  0.036  0.034  1.000  0.238 class  0.222  0.467  0.065  0.075  0.131  0.293  0.174  0.238  1.000 

7. Skew of Univariate Distributions

Skew refers to a distribution that is assumed Gaussian (normal or bell curve) that is shifted or squashed in one direction or another.

Many machine learning algorithms assume a Gaussian distribution. Knowing that an attribute has a skew may allow you to perform data preparation to correct the skew and later improve the accuracy of your models.

You can calculate the skew of each attribute using the skew() function on the Pandas DataFrame.

# Skew for each attribute importpandas url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = pandas.read_csv(url, names=names) skew = data.skew() print(skew) 

The skew result show a positive (right) or negative (left) skew. Values closer to zero show less skew.

preg     0.901674 plas     0.173754 pres    -1.843608 skin     0.109372 test     2.272251 mass    -0.428982 pedi     1.919911 age      1.129597 class    0.635017 

More Recipes

This was just a selection of the most useful summaries and descriptive statistics that you can use on your machine learning data for classification and regression.

There are many other statistics that you could calculate.

Is there a specific statistic that you like to calculate and review when you start working on a new data set? Leave a comment and let me know.

Tips To Remember

This section gives you some tips to remember when reviewing your data using summary statistics.

  • Review the numbers . Generating the summary statistics is not enough. Take a moment to pause, read and really think about the numbers you are seeing.
  • Ask why . Review your numbers and ask a lot of questions. How and why are you seeing specific numbers. Think about how the numbers relate to the problem domain in general and specific entities that observations relate to.
  • Write down ideas . Write down your observations and ideas. Keep a small text file or note pad and jot down all of the ideas for how variables may relate, for what numbers mean, and ideas for techniques to try later. The things you write down now while the data is fresh will be very valuable later when you are trying to think up new things to try.

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Summary

In this post you discovered the importance of describing your dataset before you start work on your machine learning project.

You discovered 7 different ways to summarize your dataset using Python and Pandas:

  1. Peek At Your Data
  2. Dimensions of Your Data
  3. Data Types
  4. Class Distribution
  5. Data Summary
  6. Correlations
  7. Skewness

Action Step

  1. Open your Python interactive environment.
  2. Type or copy-and-paste each recipe and see how it works.
  3. Let me know how you go in the comments.

Do you have any questions about Python, Pandas or the recipes in this post? Leave a comment and ask your question, I will do my best to answer it.

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