Reproducible analysis patterns in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the pandas library with its common alias.

Data Analysis Python

import [1] as pd

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Anumpy

Bpandas

Cmatplotlib

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Using numpy instead of pandas

Forgetting the alias 'as pd'

Importing visualization libraries instead

We use import pandas as pd to import the pandas library with the alias pd, which is a common practice for easier code writing.

2fill in blank

medium

Complete the code to read a CSV file named 'data.csv' into a DataFrame.

Data Analysis Python

df = pd.[1]('data.csv')

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Ato_csv

Bopen_csv

Cload_csv

Dread_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_csv which writes files

Using load_csv which does not exist

Using open_csv which is not a pandas function

The function pd.read_csv() reads a CSV file into a pandas DataFrame.

3fill in blank

hard

Fix the error in the code to save the DataFrame 'df' to a CSV file named 'output.csv' without the index.

Data Analysis Python

df.to_csv('output.csv', [1]=False)

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Aindex

Bno_index

Cinclude_index

Ddrop_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using include_index which is not a valid parameter

Using no_index or drop_index which do not exist

Forgetting to set index to False

The parameter index=False tells pandas not to write row numbers to the CSV file.

4fill in blank

hard

Fill both blanks to create a reproducible random sample of 10 rows from DataFrame 'df'.

Data Analysis Python

sample_df = df.sample(n=[1], random_state=[2])

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A10

B42

C100

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Not setting random_state causing different samples each run

Using too large or zero sample size

Confusing n with frac parameter

We use n=10 to select 10 rows and random_state=42 to make the sampling reproducible.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

Data Analysis Python

lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

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Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Not filtering words with length greater than 3

Using len(item) without defining item

The comprehension uses word as the key, len(word) as the value, and iterates over word in words.