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I applied via Campus Placement and was interviewed in May 2023. There were 4 interview rounds.
Commucation questions verbal , non verbal , logicam
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I applied via Naukri.com and was interviewed in Feb 2021. There were 4 interview rounds.
I applied via Naukri.com and was interviewed in Mar 2021. There were 3 interview rounds.
I applied via Recruitment Consulltant and was interviewed before Oct 2022. There were 4 interview rounds.
50 marks Aptitude test
I applied via Naukri.com and was interviewed before Apr 2023. There were 3 interview rounds.
I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.
I applied via Naukri.com and was interviewed before Mar 2023. There was 1 interview round.
On python and sql coding.they ask simple to complex questions
I applied via Referral and was interviewed before May 2023. There were 3 interview rounds.
There were 3 rounds for data science consultant position at Equifax. 1st round consists of Python and SQL questions and also in this round they have tested analytical thinking as well.
In 2nd round they have asked me questions from my resume and asked me to explain the projects which I have worked on in previous company. In this round they have asked me pandas related questions and asked me write code.
I applied via Campus Placement and was interviewed in Feb 2023. There were 2 interview rounds.
Tuples are immutable sequences of elements. A string can be converted to a tuple using the tuple() function.
Tuples are similar to lists but are immutable
Elements of a tuple are enclosed in parentheses ()
A string can be converted to a tuple using the tuple() function
Each character in the string becomes an element in the tuple
Left join is a type of join operation in SQL that returns all the rows from the left table and matching rows from the right table.
Left join is performed using the 'left join' keyword in SQL
In Python, left join can be performed using the 'merge' function from the pandas library
Syntax: pd.merge(left_dataframe, right_dataframe, how='left', on='key_column')
Example: pd.merge(df1, df2, how='left', on='id')
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