create a table in postgres and connect using python to do the following exercises in python.

Data Import and Exploration:

$-$ Import the dataset using Pandas.

$-$ Display the first few rows of the dataset.

$-$ Check the data types and missing data count for each column.

Data Cleaning and Transformation:

$-$ Handle missing values by using appropriate strategies $($e$.$g$.,$ imputation$).$

$-$ Identify and handle outliers in the dataset.

$-$ Create a new feature by applying a custom transformation to existing columns.

Data Merging and Joining:

$-$ Merge the dataset with another dataset containing customer information based on a common column.

$-$ Perform an inner join, left join, and outer join.

Time Series Data Processing:

$-$ Assuming the dataset includes a timestamp column, convert it to datetime format.

$-$ Resample the data to monthly frequency.

$-$ Apply a rolling window to calculate a $7-$day moving average.

Introduction to NumPy:

$-$ Create a NumPy array with random integer values.

$-$ Calculate the mean, maximum, and minimum of the array.

$-$ Reshape the array into a $2$x$5$ matrix.

Data Visualization with Matplotlib and Seaborn:

$-$ Create a line plot using Matplotlib to visualize the trend of a numeric feature over time.

$-$ Use Seaborn to create a box plot showing the distribution of a categorical feature.

$-$ Combine multiple visualizations $($e$.$g$.,$ scatter plot, bar chart$)$ in a single Matplotlib figure.

Handling Categorical and Numeric Data:

$-$ Apply one$-$hot encoding to a categorical feature.

$-$ Implement Min$-$Max scaling on a numeric feature.

$-$ Bin a numeric feature into discrete categories.

Data Sampling and Splitting:

$-$ Randomly sample $100$ rows from the dataset.

$-$ Split the dataset into training and testing sets using a $80-20$ split ratio.

Advanced Numeric Data Handling:

$-$ Remove outliers using the z$-$score method.

$-$ Apply log transformation to a skewed numeric feature.

$-$ Create a new feature by squaring an existing numeric feature.