numpy.ones((4, 3))

numpy.zeros((3, 4))

numpy.empty((2, 6))

numpy.array((3, 4))

int32

complex128

float64

float32

arr.first()

arr.get(0)

arr[1]

arr[0]

pd.DataFrame()

pd.createDataFrame()

pd.DataFrameBuilder()

pd.DataFrameFromDict()

df.slice(0, 5)

df.head()

df.show(5)

df.first(5)

checknull()

findMissing()

isna()

isnull()

Invoke df.clearna() to remove rows with missing data.

Apply df.drop() to discard rows with NaN entries.

Use df.remove() to eliminate rows with missing values.

Use df.dropna() to drop rows with missing values.

To group data for aggregation or transformation.

To filter out specific rows from a DataFrame.

To sort data in ascending order.

To merge two DataFrames into one.

Use the .visualize() method on a DataFrame.

Export data to Excel for visualization.

Create visualizations using Matplotlib only.

Use the .plot() method on a DataFrame or Series.

Clean the data

Understand the data

Collect the data

Visualize the data

Apply df.info() for a summary of the DataFrame's structure.

Call df.stats() to retrieve statistical information.

Use df.summary() to get a quick overview of the DataFrame.

Use df.describe() to summarize the statistics of a DataFrame in Pandas.

df.change_columns({'old_name1': 'new_name1'})

df.rename(columns={'old_name1': 'new_name1', 'old_name2': 'new_name2'})

df.set_column_labels({'old_name1': 'new_name1', 'old_name2': 'new_name2'})

df.update_column_names({'old_name2': 'new_name2'})

df.select(condition)

df[condition] where 'condition' is a boolean expression applied to a DataFrame column.

df.rows(condition)

df.filter(condition)

To eliminate all duplicate entries.

The purpose of data normalization is to standardize data formats and ensure consistency.

To enhance the visual representation of data.

To increase the size of the dataset.

Call hist() on the DataFrame directly without specifying a column.

Use DataFrame.plot.hist(column='column_name') to create a histogram.

Use DataFrame['column_name'].plot(kind='bar') to plot a histogram.

Use DataFrame['column_name'].hist() to plot a histogram of a column in a DataFrame.

It has no impact on the analysis process.

It helps in visualizing data more effectively.

It is significant for data integrity, error prevention, and performance optimization.

It is only important for large datasets.

Identify, visualize, and either cap, transform, or remove outliers based on analysis.

Always remove outliers without further investigation.

Assume outliers are errors and correct them automatically.

Ignore outliers completely and proceed with analysis.

Correlation analysis measures the average of variables.

Correlation analysis is used to predict future outcomes.

Correlation analysis eliminates outliers from the data.

Correlation analysis helps identify relationships between variables in EDA.

Use scatter plots or line plots with Pandas' plot method.

Use bar charts to compare categories.

Create pie charts for each variable.

Utilize histograms to show frequency distributions.