The primary method for retrieving data from a DataFrame involves referencing its labels. Employ the loc attribute, short for location, to indicate the specific rows and columns we wish to access:

df.loc[row_selection, column_selection]  

The loc attribute accommodates slice notation, allowing the use of a colon to select all rows or columns. Furthermore, you can utilize lists containing labels or a singular column or row name for more targeted selections. 

It is crucial to know the DataFrame, which can have one or more columns, and a Series. Even when a DataFrame has only a single column, it is still two-dimensional, whereas a Series is one-dimensional. Both the DataFrame and Series possess an index, but only the DataFrame includes column headers. When we select a column as a Series, the column header serves as the Series name. While many functions or methods can be applied to both Series and DataFrames, discrepancies arise in arithmetic calculations. 

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Write to a specific cell (loc[]) in a csv file: code:
                  
Output:         
         

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loc[] and iloc[], namely explicit index and implicit index (similar to numpy indexing): code:
        
Output:        
        

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Get maximum and minimum value of column and its index in pandas: code:          
         
Output:         
         

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Machine learning: KNN algorithm: code:
        
Input:        
        
Output:        
        

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Machine learning: KNN algorithm (version 3 -- more functions are added): code:
        
        
Input:        
        
Output:        
                

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Write a single cell with the rules (Add one more cell at the end of a specific column and then write a number into the end of the column) (code):  

Input csv file (headersOnly.csv):

Output (OutputCSV.csv):  

The code is modified to (for further test):

Input (OutputCSV.csv):

Output (OutputCSV2.csv):

The code is modified to (for further test):

Input (OutputCSV2.csv):

Output (OutputCSV3.csv):

The code is modified to (for further test) (code):

Input (OutputCSV3.csv):

Output (OutputCSV4.csv):

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