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The only 15 Python Pandas operations you need to remember:

Remembering all the Pandas operations is hard.

Remembering just a handful of them can do the trick if we want to work faster.

1. df.info() # Prints information about the DataFrame

2. df.describe() # Generates state of the columns

3. df.head() # Prints first (n) rows of the DataFrame

4. df.apply() # Applies functions on columns

5. df.groupby() # Applies aggregation of a column

6. df.sort_values() # Sorts tables

7. df.sample() # Sample data

8. pd.read_filetype(filename) # Imports from a filetype

9. df.to_filetype(filename) # Exports to a filetype

10. df.plot() # Generates plot

11. pd.to_datetime # Converts object to a datetime column

11. df.filter() # Filter on columns

12. df.drop() # Drops rows based on a condition

13. df.rank() # Generates ranks on a column

14. df1.append(df2) # Adds the rows in df1 to the df2

15. pd.isnull() # Checks the null values

https://geekycodes.in/managing-data-from-relational-databases-using-python/
Managing Data from Relational Databases using Python

Autocovariance is a statistical measure used to understand the degree of correlation between different time points within a time series.

It extends the concept of covariance to a single variable over time intervals, providing insights into how a variable is related to itself over different lags or time delays.

Essentially, autocovariance measures the linear relationship between the value of the series at one time and its values at other times.

Autocovariance is a key concept in time series analysis, especially for identifying and modeling autocorrelation, the normalized form of autocovariance.

In practical terms, understanding the autocovariance function of a time series can help in modeling the series, forecasting future values, and identifying underlying patterns that may not be immediately apparent from the raw data.

In FinCrime, identifying periods of high autocovariance in transaction data can help flag unusual patterns, such as cyclic money laundering schemes or fraud.

To find the autocovariance, you have to compare the series to itself, but with the lag taken into account. This lets you find patterns or changes that happen regularly over certain periods.

This makes autocovariance a fundamental tool in fields that deal with temporal data, including signal processing, meteorology, economics, and, indeed, any domain where understanding the temporal dynamics of a dataset is key.
Example

The negative autocovariance in our example suggests an underlying pattern that doesn't conform to regular, predictable behavior, possibly indicating financial crime.

The variability and deviation from the expected pattern could alert analysts to potential financial crime activities.

Financial institutions can take appropriate measures to mitigate risk and comply with regulatory requirements, such as anti-money laundering (AML) laws and regulations, by further investigating these anomalies