10 Questions
What are the two approaches to fill missing values in Python for Data Science?
The two approaches to fill missing values are: 1. Removing the rows or columns with missing values. 2. Imputing the missing values with a specific value like mean, median or mode of the column.
What does NaN represent in Pandas dataframes?
NaN is an acronym for 'Not a Number' and it represents missing data in Pandas dataframes.
Which functions are used to check for null values in Pandas dataframes?
The functions 'isnull()' and 'isna()' are used to check for null values in Pandas dataframes, which return a dataframe of Boolean values indicating True for NaN values.
How can the count of missing values in each column be checked in a Pandas dataframe?
The count of missing values in each column can be checked using the functions Dataframe.isna().sum() or Dataframe.isnull().sum().
What is the method to subset the rows that have one or more missing values in a Pandas dataframe?
The method to subset the rows that have one or more missing values in a Pandas dataframe is by using boolean indexing to filter the rows with missing values.
Which Python library provides a high-level interface for drawing attractive and informative statistical graphics?
Seaborn
What type of plot can be created using Seaborn library?
Scatter plot, Histogram, Bar plot, Box and whiskers plot, Pairwise plots
What is the purpose of 'numpy' library in Python for Data Science?
To do numerical operations
What is the purpose of 'pandas' library in Python for Data Science?
To work with dataframes
Which Python library is used for basic visualization in the provided text?
matplotlib
Test your knowledge of handling missing values and importing data in Python for data science. This quiz covers identifying missing values, filling missing values, and importing necessary libraries and data into Spyder for data analysis. Ideal for data science enthusiasts and beginners wanting to sharpen their Python skills.
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