Pandas is a popular Python library used for data manipulation and analysis, especially in data science. It provides a powerful and flexible data structure known as a DataFrame, which allows for easy handling of data of various types.

In this article, we will explore two main topics related to Pandas: calculating conditional mean and working with DataFrames.

## Calculating Conditional Mean in Pandas

The conditional mean is an important statistical concept used to calculate the mean of a subset of data that meets specific conditions. In Pandas, calculating conditional mean can be straightforward.

### Syntax for Calculating Conditional Mean

The syntax for calculating a conditional mean is as follows:

`df.groupby(['column_name'])['target_column'].mean()`

Here, we group the DataFrame by a specific column name, and then apply the mean function to the target column.

### Syntax Breakdown

Let’s break down the syntax into its constituent parts.

The `groupby()`

method groups the DataFrame by a specific column.

In the example above, we grouped by the `'column_name'`

.

The second part of the line `['target_column']`

specifies which column we want to calculate the mean for.

In our example, we can calculate the conditional mean for the `'target_column'`

. Finally, the `mean()`

function calculates the average of the target column.

### Example 1 – Calculating Conditional Mean for Categorical Variable

Let’s say we have a Pandas DataFrame containing data on a food delivery service. The DataFrame has two columns: the first column is the name of the restaurant, while the second column is a categorical variable indicating whether the customer rated the delivery as either ‘good’ or ‘bad’.

Let’s calculate the conditional mean for each restaurant based on their ‘good’ ratings.

```
import pandas as pd
df = pd.DataFrame({'restaurant': ['A', 'A', 'B', 'B', 'B', 'C'],
'rating': ['good', 'bad', 'good', 'good', 'good', 'bad']})
print(df.groupby(['restaurant'])['rating'].apply(lambda x: (x == 'good').mean()))
```

In this example, we grouped the DataFrame by the ‘restaurant’ column, and calculated the conditional mean for ‘good’ rated items using lambda function.

### Example 2 – Calculating Conditional Mean for Numeric Variable

Suppose we have a DataFrame that contains data on the sales of a retail store. The DataFrame has two columns: a ‘category’ column that specifies the category of the sales, and a ‘sales’ column that indicates the sales amount.

We will calculate the conditional mean of the sales column based on the ‘category’ column.

```
import pandas as pd
df = pd.DataFrame({'category': ['A', 'B', 'A', 'A', 'B', 'C', 'C'],
'sales': [1000, 1500, 2000, 2500, 3000, 2200, 1500]})
print(df.groupby(['category'])['sales'].mean())
```

In this example, we grouped the DataFrame by the ‘category’ column and calculated the average sales for each category.

## Pandas DataFrame

### Creating a Pandas DataFrame

Creating a Pandas DataFrame is an essential step when working with data in Pandas. We can create a DataFrame in several ways, including manually creating one using Python lists, by reading from a CSV file, reading from a SQL database, or using other methods.

Manually creating a DataFrame involves specifying a Python dictionary, where each key represents a column name and each value represents a list of values for that column.

```
import pandas as pd
data = {'name': ['Bob', 'Alice', 'John'],
'age': [25, 30, 28],
'gender': ['M', 'F', 'M']}
df = pd.DataFrame(data)
```

### DataFrame Structure and Syntax

Pandas DataFrame has a well-defined structure that is easy to use. Each column in the DataFrame represents a Pandas Series and has a unique name.

All columns have an equal number of elements, and each row represents a unique data point. All DataFrame columns have the same data type, which could be numeric, boolean, or categorical.

DataFrames also have an index, which represents the row labels. We can access individual columns of a Pandas DataFrame using a bracket notation, with the syntax `df['column_name']`

.

If we want to select multiple columns, we can use a list of column names, like this: `df[['column_name_1', 'column_name_2', ...]]`

.

### Viewing and Accessing DataFrame

We can view various attributes of a Pandas DataFrame to check the structure of the data. For instance, we can use the `head()`

and `tail()`

methods to view the first and last rows of the DataFrame, respectively.

We can also use the `.iloc[]`

method to access specific rows and columns within a Pandas DataFrame. `iloc[]`

is used to select a subset of the DataFrame based on the integer index.

Using `loc[]`

, we can access DataFrame elements based on their corresponding index values, which could be string or integer values. In conclusion, Pandas provides an effective way to handle data and perform essential data manipulation tasks.

Understanding how to calculate conditional means and work with DataFrames is crucial in data analysis. With sufficient practice and application, you can become proficient in using Pandas to extract insights and meaningful information from data.

### Example 1 – Calculating Conditional Mean for Categorical Variable

In this section, we will provide a more in-depth look at calculating the conditional mean for a categorical variable in Pandas. We will provide the code to execute this calculation, and demonstrate how to verify the results manually.

#### Code to Calculate Conditional Mean for Categorical Variable

Recall that to calculate the conditional mean for a categorical variable, we use the syntax:

```
df.groupby(['column_name'])['target_column'].mean()
```

Let’s use a concrete example to illustrate this. Suppose we have a DataFrame named `df`

that contains customer rating data for a food delivery service.

The DataFrame has two columns: the name of the restaurant (`restaurant`

) and a categorical variable indicating whether the customer rated the delivery as either ‘good’ or ‘bad’ (`rating`

).

```
import pandas as pd
df = pd.DataFrame({'restaurant': ['A', 'A', 'B', 'B', 'B', 'C'],
'rating': ['good', 'bad', 'good', 'good', 'good', 'bad']})
```

To calculate the conditional mean for each restaurant based on their ‘good’ ratings, we can use the `groupby()`

method along with the `mean()`

method on the `'rating'`

column.

```
good_rating_means = df.groupby(['restaurant'])['rating'].apply(lambda x: (x == 'good').mean())
print(good_rating_means)
```

In this example, we grouped the DataFrame by the `'restaurant'`

column using `groupby()`

. We then applied the `mean()`

method on the `'rating'`

column to get the average value for each group.

The `apply()`

method was then used with a lambda function to calculate the fraction of `'good'` ratings.`

```
```#### Manual Verification of Mean Calculation

It is often advisable to verify results manually to ensure the correctness of the calculated values. To manually verify the mean calculation for the `'restaurant'`

column, we can execute the following code:

Copy
```
res_rest_rating = {'A': [1, 1, 0],
'B': [1, 1, 1],
'C': [0, 1, 0]}
for rest in res_rest_rating:
print(f"The conditional mean for restaurant {rest} is {res_rest_rating[rest][0]/sum(res_rest_rating[rest])}")
```

In this snippet of code, we manually created a dictionary `res_rest_rating`

to store the number of `'good'` and ``'bad'` ratings for each restaurant in the form ``[good_counts, bad_counts, total_counts]`

.

```
```We then looped through each restaurant in the `res_rest_rating`

dictionary and computed the conditional mean as the ratio of `'good'` ratings to total ratings.`

```
```### Example 2 - Calculating Conditional Mean for Numeric Variable

In contrast to a categorical variable, a numeric variable can take on a wide range of values. Nonetheless, calculating the conditional mean for numeric variables is just as easy in Pandas.

In this section, we will provide the code to calculate the conditional mean for numeric variables and demonstrate how to verify the results manually.

#### Code to Calculate Conditional Mean for Numeric Variable

#### Recall that calculating the conditional mean for a numeric variable can be done using the syntax:

Copy
```
df.groupby(['column_name'])['target_column'].mean()
```

Building on our previous example, let's assume we now have a DataFrame named `df_sales`

that contains sales data for a retail store. The DataFrame has two columns: the product `category`

and a numeric variable indicating the total `sales`

of each product.

Copy
```
import pandas as pd
df_sales = pd.DataFrame({'category': ['A', 'B', 'A', 'B', 'B', 'C', 'C'],
'sales': [1000, 1500, 2000, 2500, 3000, 2200, 1500]})
```

To calculate the conditional mean of 'sales' for each category, we can use the `groupby()`

method along with the `mean()`

method on the `'sales'`

column.

Copy
```
sales_means = df_sales.groupby(['category'])['sales'].mean()
```

In this example, we grouped the DataFrame by the `'category'`

column using `groupby()`

.

We then used the `mean()`

method on the `'sales'`

column to calculate the average value for each group.

#### Manual Verification of Mean Calculation

As with categorical variables, we can manually verify the calculated conditional means for numeric variables. To verify the calculated means, we can use group the DataFrame by category and print out the average value of sales for each product category:

Copy
```
res_sales = {'A': [1500.0, 2],
'B': [2333.33, 3],
'C': [1850.0, 2]}
for cat in res_sales:
print(f"The conditional mean for category {cat} is {res_sales[cat][0]}")
```

In this code block, we manually created a dictionary `res_sales`

to store the average sales for each product category and the count of items in each category.

We then looped through each category in `res_sales`

and printed the conditional mean for each category. Note that the manually computed means match the results we obtained with Pandas.

## Conclusion

In conclusion, Pandas provides effective tools to compute conditional means for both categorical and numerical variables. In this article, we have demonstrated how to compute the conditional mean for both categorical and numerical data through code and manual verification.

Understanding how to calculate conditional means for different datasets can improve data analysis, and we hope this article has helped clarifying how to use Pandas to achieve these tasks. In this article, we explored the topics of calculating conditional mean and working with Pandas DataFrames.

We began by examining how to calculate conditional means for categorical and numerical variables using Pandas. We provided users with relevant code, and also demonstrated how to manually verify the results.

We then went on to examine how to create, structure, access, and view DataFrames in Pandas. In conclusion, understanding Pandas and its tools is important for effective data analysis, and this article provides users with the necessary knowledge needed to excel in data analysis using Pandas.

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