Applied Statistics and Data Preparation with Python

This chapter gets you started with Python, showing how to install and use libraries for data manipulation, transformation, language processing, and visualization, plus IDE basics.

Kick off your journey in applied statistics and data preparation with Python, exploring introductory steps in getting started part two.

Get started with Python data tools by installing and configuring the Python interpreter, using Jupyter on Unix, and exploring initial data preparation workflows and ideas.

Learn the basics of python coding, run scripts in a console, print outputs like hello, and explore simple random and button interactions as you complete chapter 2.

Explore the data mining process from business understanding to deployment, including domain experts, data understanding, preparation, modeling with classification and regression, and evaluation with accuracy.

Download and work with the iris CSV dataset and a high-risk dataset, and learn safe data access and preparation steps for statistical learning in Python.

Import pandas and read a csv file using read_csv. Append data to build variables for analysis and use visualization libraries like seaborn to print and explore the results.

Learn how mode, as the most frequent value in a dataset, complements descriptive statistics to summarize data; apply Python code to compute the mode and explore central tendency.

Explore computing medians for columns in a dataset using the median function in Python, and interpret the medians for different variables.

Learn how to compute the mean as the data's average using a mean function in Python, applying it to a single variable and understanding data preparation workflows.

Identify range as the difference between the largest and smallest values, compute with max and min functions, and print and compare the min and max to understand data spread.

Compute the range of a single column by subtracting the minimum from the maximum in Python. Adapt the calculation to other variables by replacing the column name.

Learn to compute quantiles in Python, including the 25th, 50th, and 75th percentiles for a single variable, using a simple function to extract these values.

Define variance as the average of the scale differences from the mean, and compute it for a single column or separate line variable using simple steps.

Discover how to compute the standard deviation for a single variable using the std function, by specifying the column name and selecting the appropriate column.

Explain the normal distribution and its bell-curve shape, and demonstrate creating a histogram with Python using numpy and pandas to visualize data.

Learn how to interpret a QQ plot to assess whether data follow a normal distribution and compare theoretical versus observed quantiles using probability concepts.

Apply the shapiro test in python to assess normality, interpret p-values against the null hypothesis, and determine whether data deviate from a normal distribution.

Explore skewness in single-variable data using Python, applying simple functions to prepare and analyze data for statistical insights.

Kurtosis measures how symmetrical the distribution is and relates to skew, noting negative skew (left) and positive skew (right), and helps assess tail heaviness.

Describe function highlights descriptive statistics for a dataset, returning the mean, standard deviation, and quartiles such as the 25th and 75th percentiles, plus the maximum.

Explore correlation as a measure of association between two variables, connect it to inferential statistics and regression analysis, and implement Pearson correlation in Python to quantify linear relationships.

Explore covariance, a measure of variability between two variables, and learn to compute it using R, with notes on variances.

Explore one-sample t tests, null and alternate hypotheses, and how to interpret p-values to decide whether to reject the null, given assumptions of random sampling and normality.

Explore the two-sample t-test to compare means, interpret p-values, and decide whether to reject the null hypothesis based on sample data.

Perform a two-sample t-test with equal variances to compare means, interpret p-values against alpha, and decide whether to reject the null hypothesis.

Explore the chi-square test to assess whether categorical variables are related, compute chi-square statistic and p value in Python, and interpret the null hypothesis of independence with degrees of freedom.

Apply one-way anova to test whether means differ across two or more groups, using the null hypothesis of equal means and interpreting the p-value and 95% confidence interval.

Apply linear regression in Python to relate a dependent variable to independent variables, and interpret coefficients, intercept, p-values, and confidence intervals under a null hypothesis.

Extend simple regression to multiple linear regression by adding predictor variables and estimating coefficients and the constant. Interpret p-values, 95 percent confidence intervals, and standard errors to assess model fit.

Learn date preparation and data processing in Python after importing data, including selecting data, handling missing values, and inspecting data. Use df.head(10) to view the first 10 rows.

Learn data processing with Python by applying df.tail to obtain the last 10 rules, illustrating how to select specific subsets for applied statistics and data preparation.

Learn how to select rows in Python data processing by adjusting range and numeric parameters to tailor datasets.

Master selecting rows and multiple variables in Python data processing, with practical examples to pick two variables and refine your data analysis workflow.

Process data in Python by removing variables through dropping columns to simplify datasets during data preparation.

Learn how to append rows to data in Python using pandas, supporting data preparation tasks and practical steps for managing data.

Apply Python data processing to sort variables and columns, using slot values and variable names, and apply ascending or descending order to organize data.

Learn data processing with Python groupby to group records, apply functions, and compute means across groups, capturing robust values and species-specific summaries.

Remove missing values by dropping them with a function, applying the method in your data processing workflow to handle missing data in Python.

Learn how to detect missing values in Python with a function and remove them to clean data for analysis.

Learn to replace missing values in data processing with Python, applying strategies to prepare datasets for analysis.

Learn how to remove duplicates in Python using the drop duplicates function, assign the result to a variable, and execute the code.