**Step 1: Install the required packages**

Make sure you have Pandas and NumPy installed in your Django project. You can install them using pip:

```
pip install pandas numpy
```

**Step 2: Import the required libraries**

In your Django view or script, import the necessary libraries:

```
import pandas as pd
import numpy as np
```

**Step 3: Load the data**

Assuming you have a CSV file named "data.csv" in your Django project directory, you can load the data using Pandas:

```
data = pd.read_csv('data.csv')
```

**Step 4: Data cleaning**

Perform data cleaning operations as needed. Here are some common data cleaning tasks:

Handling missing values:

```
data.dropna() # Drop rows with missing values
data.fillna(value) # Fill missing values with a specific value
```

**Removing duplicates**:

```
data.drop_duplicates() # Remove duplicate rows
```

**Removing outliers**:

```
data = data[(np.abs(data['column']) < 3 * np.std(data['column']))]
```

**Remove outliers based on a threshold**

**Step 5: Data processing**

Perform data processing operations based on your requirements. Here are some examples:

**Filtering data**:

```
filtered_data = data[data['column'] > threshold]
```

**Filter rows based on a condition**

Calculating statistics:

```
mean_value = data['column'].mean()
```

**Examples**

```
import pandas as pd
# Create a sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Emily'],
'Age': [25, 32, 28, 35],
'Salary': [50000, 70000, 60000, 80000]}
df = pd.DataFrame(data)
# Calculate the mean value of the 'Salary' column
mean_value = df['Salary'].mean()
print(mean_value)
```

**Output**:

```
65000.0
```

**Calculate the mean of a column**

**Applying transformations**:

```
data['new_column'] = np.sqrt(data['column'])
```

```
import pandas as pd
import numpy as np
# Create a sample DataFrame
data = {'Column1': [4, 9, 16, 25, 36]}
df = pd.DataFrame(data)
# Calculate the square root of the 'Column1' column and assign it to a new column 'NewColumn'
df['NewColumn'] = np.sqrt(df['Column1'])
print(df)
```

**Output**:

```
Column1 NewColumn
0 4 2.000000
1 9 3.000000
2 16 4.000000
3 25 5.000000
4 36 6.000000
```

**Apply a square root transformation to a column**

**Step 6: Store the processed data**

Store the cleaned and processed data back into the Django models or export it to a file. For example, if you have a Django model named DataModel, you can store the processed data as follows:

```
for index, row in filtered_data.iterrows():
obj = DataModel(field1=row['column1'], field2=row['column2'])
obj.save()
```

Alternatively, you can export the processed data to a CSV file:

```
filtered_data.to_csv('processed_data.csv', index=False)
```

That's it! You have now performed data cleaning and processing using Pandas and NumPy in Django. Feel free to customize the code based on your specific requirements and the structure of your data.

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