In order to clean data, there are a number of things we can do to analyze the data we have loaded in Data Visualization.
df.head() This returns the first few rows of the data set and all columns. For the last few columns df.tail()
df.info() prints information about the DataFrame.
df.columns provides access to the column labels of a data frame. Here we can manipulate the data, such as dropping columns.
df = df[df.columns.drop(list(df.filter(regex='url')))]
df.shape
df.isnull() will check for null data that you may want to remove or define a default value.
df.drop(), df.rename(), df.info() can help clean data
The revitalized art gallery is set to redefine cultural landscape.
Data cleaning is important because it ensures that data is accurate, consistent, and high quality, which can help improve the results of analysis and decision-making.