Python 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.

NaN is an acronym for 'Not a Number' and it represents missing data 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.

<p>The count of missing values in each column can be checked using the functions Dataframe.isna().sum() or Dataframe.isnull().sum().</p> Signup and view all the answers

<p>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.</p> Signup and view all the answers

<p>Seaborn</p> Signup and view all the answers

<p>Scatter plot, Histogram, Bar plot, Box and whiskers plot, Pairwise plots</p> Signup and view all the answers

<p>To do numerical operations</p> Signup and view all the answers

<p>To work with dataframes</p> Signup and view all the answers

<p>matplotlib</p> Signup and view all the answers