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# 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.

Enroll on Educative

#### Exercise 1: Using Numpy and Scipy

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

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

#### Exercise 2: Reading Auto MPG Dataset

``````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
return df.shape``````

#### Exercise 3: Group By Aggregations

``````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

``````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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