**Descriptive Statistics in Python**

Descriptive statistics include those that summarize the central tendency, dispersion, and shape of a dataset’s distribution.

- Measure of central tendency
- Measure of spread/dispersion
- Measure of symmetry [ will save this for the future post]

### Dataset

Imported all the libraries needed for statistical plots and created a dataframe from the dataset given in `bmi.csv `

file.

This dataset contains Height, Weight, Age, BMI, and Gender columns. Let’s calculate descriptive statistics for this dataset.

The code used in this project is available as a Jupyter Notebook on GitHub.

importnumpyasnpimportpandasaspdimportmatplotlib.pyplotasplt % matplotlib inline

df=pd.read_csv("bmi.csv") df

### Measure of Central Tendency

Measure of central tendency is used to describe the middle/center value of the data.

`Mean, Median, Mode`

are measures of central tendency.

**1. Mean**

- Mean is the
`average value`

of the dataset. - Mean is calculated by adding all values in the dataset divided by the number of values in the dataset.
- We can calculate the mean for only numerical variables

**Formula to calculate mean**

**Calculating the mean of the “Age” column in our dataset.**

**Mathematical Calculation**

**Calculating Mean for a particular variable (“Age”) using Python.**

df["Age"].mean()

**Output:** `28.333333333333332`

**Calculating the mean for all the columns in the dataframe.**

df.mean()

Calculated mean only for numerical datatype. “Gender” column in the dataset is excluded.

**2. Median**

- The Median is the
`middle number`

in the dataset. - Median is the best measure when we have outliers.

**Find the median of the “Age” column in our dataset.**

**Mathematical Calculation**

If we have an even number of data, find the average of the middle two items.

**Example:**

Age → 4,12,24,8,16,20

Sort → 4,8,**12,16**,20,24

Pick the middles ones → 12,16

Find the average → 28/2 =14

**Calculating the median for a particular variable (“Age”) using Python.**

df["Age"].median()

**Output: **28

**Calculating the median for all the columns in the dataframe.**

df.median()

Calculated median only for the numerical datatype. The “Gender” column in the dataset is excluded.

**3. Mode**

The mode is used to find the common number in the dataset.

**Calculating mode for a particular variable (“Age”) using a mathematical calculation**

**Calculating the mode for a particular variable (“Age”) using Python.**

df["Age"].mode()

**Calculating the mode for all the columns in the dataframe.**

df.mode()

Here, in `bmi`

column, all numbers are unique. So, all numbers are displayed.

`Weight `

column,70.0, and 80.0 are repeated more times, so both are displayed.

### Measure of spread

- The measure of spread/dispersion is used to describe how data is spread. It also describes the
**variability**of the dataset. **Standard Deviation, Variance, Range, IQR,**are used to describe the measure of spread/dispersion- The measure of spread can be shown in graphs like
**boxplot**.

#### 1.Variance

Variance is used to describe how far each number in the dataset is from the mean.

**Formula to calculate population variance**

σ2 — Population Variance

μ -Population Mean

N -Total number of data in the dataset.

**Calculating variance for “Age” column in the dataset**

df["Age"].var()

**Output:** 5.5

**Calculating variance for all columns in the dataframe**

df.var()

Calculated variance only for numeric data types. The “Gender” column is excluded.

#### 2.Standard Deviation

- Standard Deviation is the measure of the spread of data from the mean.
- Standard deviation is the square root of variance.
- More the standard deviation, more the spread.

**Calculating the standard deviation for ****“Age” ****column in the dataset**

df["Age"].std()

**Output**: 2.345207879911715

**Calculating the standard deviation for all columns in the dataset.**

df.std()

**3.Range**

- The range is the difference between the largest number and the smallest number
- Larger the range, the more the dispersion.

**Calculating the range of “Age” column in the dataset**

m1=df["Age"].max() m1#Output:32m2=df["Age"].min() m2#Output:25range=m1-m2 range#Output: 7

#### 4. Interquartile range (IQR)

- Quartiles describe the spread of data by breaking into quarters. The median exactly divides the data into two parts.
**Q1(Lower quartile)**is the middle value in the first half of the sorted dataset.**Q2**– is the median value**Q3 (Upper quartile)**is the middle value in the second half of the sorted dataset- The interquartile range is the difference between the 75th percentile(Q3) and the 25th percentile(Q1).
- 50% of data fall within this range.

**Calculating IQR for ****“Age”**** column in the dataset.**

Q1=df["Age"].quantile(0.25) Q1#Output : 27.0Q3=df["Age"].quantile(0.75) Q3#Output: 30.0IQR=Q3-Q1 IQR#Output: 3.0

#### describe()

describe() function generates descriptive statistics.It is used to view some basic statistical details like mean, median, min, max, percentiles, count of a dataframe, or series of numeric values.

**Series**

df["Age"].describe()

**2. Dataframe**

df.describe()

25% → Q1-Lower quartile

50% → Median

75% → Q3-Upper quartile

3.` include=”all”`

All columns of the input will be included in the output.

df.describe(include="all")

#### Five-point summary

The five-point summary consists of five values

- Minimum value
- Q1 -Lower quartile
- Median
- Q3-Upper quartile
- Maximum value

### Statistical Plots

Statistical plots are used to identify outliers, visualize distributions, discover relationships, and the correlation between variables in a dataset.

#### Boxplots

Boxplot is used to describe how the data is distributed in the dataset. This graph represents `five-point summary`

(minimum, maximum, median, lower quartile, and upper quartile). This graph is used to identify **outliers**.

- whiskers — denote the spread of data
- box — represents the IQR- 50% of data lies within this range

df.boxplot(column="Age")

**Output:**

**Explaining the boxplot**

In the “Age” category, no outliers are there.

#### Boxplot grouped by gender category

df.boxplot(column="Age",by="Gender")

Here we have an outlier in the “Male” category.

Let’s calculate a five-point summary for` “Age”`

under

category**“Male”**

#### Calculating Mean of “Age” of Male alone.

df1=df.set_index("Gender") df2=df1.loc["Male","Age"] df2.mean() #Output: 28.0

#### Calculating Q1, Q3, and IQR

q1=df2.quantile(0.25) print ("Q1 :",q1) q3=df2.quantile(0.75) print ("Q3 :",q3) IQR=q3-q1 print ("IQR :",IQR

**Output:**

Q1 : 27.0 Q3 : 28.75 IQR : 1.75

**Calculate the length of the upper whisker**

The length of the upper **whisker** is the largest value that is no greater than the` third quartile(Q3)`

plus 1.5 times the` interquartile `

**range(IQR)**

whisker=(IQR*1.5)+Q3 print("Length of Upper Whisker :",whisker)#Output:Length of Upper Whisker : 31.375

The length of the upper whisker should not be greater than 31.375

Age of Male → 25,27,28,29,**32**,27

**32** falls above the **upper whisker range**. So, it’s an** outlier.**

So the length of the upper whisker is taken as **29**(second maximum) which falls under the upper whisker range.

#### Boxplot using seaborn

sns.boxplot(x="Age",data=df)

#### Boxplot grouped by gender category

sns.boxplot(x="Age",y="Gender",data=df)

**Visualizing distributions of data**

### Plotting univariate distributions

**Histogram**

- This plot will show the
**distribution of univariate**(single variable). - A histogram is a bar plot where the axis representing the data variable is divided into a set of discrete bins and the count of observations falling within each bin is shown using the height of the corresponding bar.
- Histogram → plotting
**variable**vs their**count/frequencies**in each bin.

**Different ways to plot a histogram**

**Using pandas**

**Histogram for all columns in the dataframe**

df.hist()

#### Histogram for a particular column “Age”

df["Age"].hist()

In the above graph, only age **27 **appears twice, so the count of age **27** is shown as **2**. Remaining all ages appear once only. So all other age count is shown as 1.

### Choosing the bin size

df["Age"].hist(bins=20)

#### 2. Using matplotlib

plt.hist(df["Age"])

### Plotting Multivariate distribution

**Pairplot or Scatterplot**

Pairplot is used to describe pairwise relationships in a dataset. Pairplot is used to visualize the univariate distribution of all variables in a dataset along with all of their pairwise relationships.

The diagonal plots are histograms and all the other plots are scatter plots.

sns.pairplot(df)

### Resources

seaborn — distributions

pandas.Series.hist

panda.DataFrame.hist

matplotlib-hist

describe()

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