This quiz covers the following topics:

- Reading a CSV file into a Pandas data frame
- Retrieving data from Pandas data frames
- Querying, sorting and analyzing data
- Merging, grouping, and aggregation of data
- Extracting useful information from dates
- Writing data frames to CSV files

Analyzing Tabular Data with Pandas

Which of the following is a good conceptual model for how data is stored in a Pandas dataframe?

All values in a column of a dataframe must have the same datatype.

You can retrieve the list of values in a specific column of a dataframe using the __ notation.

Each column in a dataframe is represented using a data structure called __

A pandas series conceptually similar to __ ?

Which of the following are valid ways to retrieve an element located at a specific row or column or both in a Pandas dataframe?




How to access a list of columns from a data frame?

For example: from the below given data frame, access only Strings, Numbers and Runs columns

Choose the correct statements for the following syntax:

```jovian_notebooks_new = jovian_notebooks.copy()```

Choose the correct statements for the following syntax:

```jovian_notebooks_new = jovian_notebooks```

For the given data frame, match the following syntax to their result.
<iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=95&hideInput=true" title="Jovian Viewer" height="380" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

1. ```jovian_notebooks.index```

2. ```jovian_notebooks.at['e','Year']```

3. ```jovian_notebooks.iloc[6]['Year']```

4. ```jovian_notebooks.loc['f']['Year']```

5. ```jovian_notebooks.tail(5)```

6. ```jovian_notebooks.head(5)```

7. ```jovian_notebooks['Likes']```

a. ```2021```

b. ```2018```

c. ```2020```

d. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/2&cellId=2&hideInput=true" title="Jovian Viewer" height="247" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

e. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=94&hideInput=true" title="Jovian Viewer" height="247" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

f. ```Index(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'], dtype='object')```

g. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/2&cellId=65&hideInput=true" title="Jovian Viewer" height="290" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

Choose the correct statements.

We can use the __ method to retrieve a random sample of rows from the data frame.

In a data frame with Likes, Runs as columns, you need to get 20 notebooks with the maximum number of likes. Choose the correct syntax.

Match the most appropriate way of handling the missing data for each scenario.

1. When the data is skewed, it is good to consider using the ___ for replacing the missing values

2. For data like salary ___ is considered for replacing the missing values

3. ___ not a recommended technique for imputing missing values when data is skewed

4. For categorical data, ___ technique can be used


a. mean

b. median and mode

c. median

d. mode

How to convert a column to datetime datatype?

For the given data frame, match the following syntax to their results.

<iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=95&hideInput=true" title="Jovian Viewer" height="380" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

1. ``` jovian_notebooks[['Notebook','Likes']].mean() ```

2. ``` jovian_notebooks[['Notebook','Likes']].sum() ```

3. ``` jovian_notebooks.groupby('Notebook')[['Likes']].mean() ```

4. ``` jovian_notebooks.groupby('Notebook')[['Likes']].sum() ```

a. <code> Likes    4670.0 </code>

   <code> dtype: float64  </code>

b.<code> Notebook    Python ProgrammingStatisticsData AnaylsisData ...</code>

  <code> Likes                                                   46700</code>

  <code> dtype: object </code>

c. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=57&hideInput=true" title="Jovian Viewer" height="327" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

d. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=58&hideInput=true" title="Jovian Viewer" height="327" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

For the given data frame, match the following arithmetic operation syntax to their results.

<iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/1&cellId=95&hideInput=true" title="Jovian Viewer" height="380" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

1. ```jovian_notebooks['Runs'].count()```

2. ```jovian_notebooks['Runs'].max()```

3. ``` jovian_notebooks['Runs'].mean()```

4. ``` jovian_notebooks['Likes'].mean()```

5. ``` jovian_notebooks['Likes'].sum()```

a. ```45k```

b. `10k`

c. `10`

d. `4670`

e. `ValueError`

f. `46700`

For the given data frame, match the following arithmetic operation syntax to their results.

<iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/27&cellId=36" title="Jovian Viewer" height="418" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>


Match the following results to their Syntax

1. ```jovian_notebooks.groupby('Notebook')[['Likes']].sum().reset_index```

2. ```jovian_notebooks['Likes'].cumsum()```

3. ```jovian_notebooks.groupby('Likes')[['Likes']].cumsum().reset_index```

4. ```jovian_notebooks['Likes'].sum()```

a. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/27&cellId=32&hideInput=true" title="Jovian Viewer" height="248" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

b. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/27&cellId=34&hideInput=true" title="Jovian Viewer" height="290" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

c. <iframe src="https://jovian.ai/embed?url=https://jovian.ai/samanvitha/quiz-c39b8/v/27&cellId=33&hideInput=true" title="Jovian Viewer" height="290" width="100%" style="margin 0 auto; max-width: 800px;" frameborder="0" scrolling="auto"></iframe>

d. ```46700```


Match the following syntax to their working

1. Write to a excel file

2. Read a csv file

3. Read a excel file

4. Write to a csv file

a. ```pd.read_csv(path)```

b. ```df.to_csv(df.csv)```

c. ```pd.read_excel(path)```

d. ```df.to_excel(df.xlsx)```

What is the difference between `df.to_csv()` and `df.to_csv(index=None)`?

How to reset the index of a dataframe in pandas?

What is `inplace=True` in `df.drop('column',inplace=True)`?

The index of a data frame can be only numeric.

How to remove a column from the data frame? 

How to add a new column to the data frame?