Categorical variables can be either ordinal (ordered) or nominal (unordered).

Examples of ordinal variables include places (1st, 2nd, 3rd) and survey responses (on a scale of 1 to 5, how much do you agree with a statement).

Examples of nominal variables include tree species, student names, and account names.

The pandas method, `.describe()`

provides summary statistics for all features in a dataset. Setting `include = 'all'`

includes summary statistics for both quantitative and categorical features.

df.describe(include = 'all')

To summarize the central tendency, or typical value, of a quantitative variable, we can use statistics such as the mean, median, and mode. These can be calculated using the pandas methods `.mean()`

, `.median()`

, and `.mode()`

, respectively.

#calculate mean of a columndf.column_name.mean()#calculate median of a columndf.column_name.median()#calculate mode of a columndf.column_name.mode()

To summarize the spread, or variation, of a quantitative variable, we can use statistics such as the range, interquartile range, variance, standard deviation, and mean absolute deviation. These can be calculated as shown.

#calculate range of a columndf.column_name.max() - df.column_name.min()#calculate IQR of a columndf.column_name.quantile(0.75) - df.column_name.quantile(0.25)#calculate variance of a columndf.column_name.var()#calculate standard deviation of a columndf.column_name.std()#calculate MAD of a columndf.column_name.mad()

To inspect the distribution of a quantitative variable, we can use visualizations such as histograms and box plots. We can create these plots using the seaborn functions `histplot()`

and `boxplot()`

, respectively.

import matplotlib.pyplot as pltimport seaborn as sns#create histogramsns.histplot(x = 'column_name', data = data_name)plt.show()#create boxplotsns.boxplot(x = 'column_name', data = data_name)plt.show()

To summarize the distribution of a categorical/discrete feature, we can calculate the number or proportion of observations in each category using the pandas method `.value_counts`

.

#calculate the number in each categorydf.column_name.value_counts()#calculate the proportion in each categorydf.column_name.value_counts(normalize = True)

To inspect and explore categorical features, we can use visualizations such as bar charts or pie charts. The provided code demonstrates how to create these plots.

import matplotlib.pyplot as pltimport seaborn as sns#create bar chartsns.countplot(x = 'column_name', data = data_name)plt.show()#create pie chartdf.column_name.value_counts().plot.pie()plt.show()