Python Data Analysis and Visualization Educative Quiz Answers

Get Python Data Analysis and Visualization Educative Quiz Answers

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

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Exercise 1: Using Numpy and Scipy

Numpy Answer:

def perform_calculations(array):
    return np.max(array), np.std(array), np.sum(array), np.dot(array, array)

Scipy Answer:

def correlation(array1, array2):
  return stats.pearsonr(array1, array2)

Exercise 2: Reading Auto MPG Dataset

Answer:

def read_csv():
    # Define the column names as a list
    names = ["mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name"]
    # Read in the CSV file from the webpage using the defined column names
    df = pd.read_csv("auto-mpg.data", header=None, names=names, delim_whitespace=True)
    return df.shape

Exercise 3: Group By Aggregations

Answer:

def group_aggregation(df, group_var, agg_var):
    grouped_df = df.groupby([group_var])[agg_var].mean()
    return grouped_df

Exercise 4: Cleaning Auto MPG Dataset

Answer:

def outlier_detection(df):
    df = df.quantile([.90, .10])
    return df

Quiz: Test Yourself

Q1. Which Seaborn function creates scatter plots?

  • lmplot function
  • distplot function
  • lineplot function
  • heatmap function

Q2. When is a bar plot useful?

  • When comparing values across time
  • When comparing categories
  • When plotting a variable’s distribution
  • When visualizing correlations between variables

Q3. What value is the maximum whisker value of a box plot?

  • Median
  • 75th percentile
  • Mean
  • 75th percentile + 1.5 * the inner-quartile range

Q4. When is the FacetGrid and map combination useful?

  • To visualize a matrix of data
  • To visualize multi-plot grids
  • To visualize a distribution
  • To create bar plots
Conclusion:

I hope this Python Data Analysis and Visualization Educative Quiz Answers would be useful for you to learn something new from this problem. If it helped you then don’t forget to bookmark our site for more Coding Solutions.

This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.

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