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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

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 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A
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How to use Pandas Series Method top ideas

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How to use Pandas Series Method top ideas

Here is an example of how to use a Series constructor in Pandas. A one-dimensional array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.) is called a Series object in pandas.

Sample DataFrame




Single dimension data


Below is the single dimension data of Index and Value.


 Index Value
 1 10           
 2 40
 3 01
 4 99

Having single value for an index is called Single dimensional data. On the other hand, when one index has multiple values, it is called multi-dimensional array.  

Below is the example for Multi-dimensional array. 

a = (1, (10,20))
mySeries = pd.Series(data, index=index)
Here, pd is a Pandas object. The data and index are two arguments. The data refers to a Python dictionary of "ndarray"  and index is index of data.

Generating DataFrame from single dimension data

The below example shows, how to construct single dimension data (Values and Index).

>>>mySeries = pd.Series([10,20,30], index=[1,2, 'a'])

Special Notes: In the above index list the 'a' represents alpha type.

Once mySeries object created, you can verify Values and Index. Do follow the steps in the screen.

series data 

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