# Summarizing a Single Feature

### Pandas .describe() method

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')

### Central tendency statistics

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 column
df.column_name.mean()
#calculate median of a column
df.column_name.median()
#calculate mode of a column
df.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 column
df.column_name.max() - df.column_name.min()
#calculate IQR of a column
df.column_name.quantile(0.75) - df.column_name.quantile(0.25)
#calculate variance of a column
df.column_name.var()
#calculate standard deviation of a column
df.column_name.std()

### Visualize the distribution of a quantitative/continuous feature

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 plt
import seaborn as sns
#create histogram
sns.histplot(x = 'column_name', data = data_name)
plt.show()
#create boxplot
sns.boxplot(x = 'column_name', data = data_name)
plt.show()

### Summary statistics for categorical data

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 category
df.column_name.value_counts()
#calculate the proportion in each category
df.column_name.value_counts(normalize = True)

### Visualizing categorical data

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 plt
import seaborn as sns
#create bar chart
sns.countplot(x = 'column_name', data = data_name)
plt.show()
#create pie chart
df.column_name.value_counts().plot.pie()
plt.show()

### Ordinal and Nominal Categorical Data

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.