**Dropping Missing Data in Python**
One of the easiest ways to handle missing data is by using the `dropna()` function in pandas. This function allows you to drop rows or columns that contain missing values.
To use this function, you simply need to select the row or column you want to drop and then call `dropna()`. For example, if you have a DataFrame called `df` and you want to drop all rows that contain missing values, you can do so by calling `df.dropna()`. This will return a new DataFrame with all rows containing missing values removed.
For instance, let's say we have a DataFrame called `df` with 333 roles. We can use the `dropna()` function to drop all rows that contain at least one missing value. By doing so, we would be left with only 325 rows in our DataFrame because there are eight missing values in total.
To implement this, you would use the following code:
```python
import pandas as pd
# create a sample DataFrame
df = pd.DataFrame({
'role': ['1', '2', '3', '4', '5', '6', '7', '8'],
'species': ['A', None, 'C', 'D', 'E', 'F', 'G', 'H']
})
# drop rows with missing values
df = df.dropna()
print(df)
```
In this example, we first create a sample DataFrame called `df`. We then use the `dropna()` function to remove all rows that contain at least one missing value. Finally, we print the resulting DataFrame to see the updated number of rows.
**Handling Missing Data**
Another way to handle missing data is by replacing it with a new value. This can be useful if you want to impute missing values in a specific way.
For example, let's say you have a numerical column called `salary` and you want to replace all missing values with the mean of the column. You can do this using the following code:
```python
import pandas as pd
# create a sample DataFrame
df = pd.DataFrame({
'role': ['1', '2', '3', '4', '5', '6', '7', '8'],
'salary': [50000, None, 60000, 70000, 80000, 90000, 100000, 110000]
})
# replace missing values with the mean of the column
df['salary'] = df['salary'].fillna(df['salary'].mean())
print(df)
```
In this example, we first create a sample DataFrame called `df`. We then use the `fillna()` function to replace all missing values in the `salary` column with the mean of the column. Finally, we print the resulting DataFrame to see the updated values.
**Saving the Updated DataFrame**
When you make changes to a DataFrame using functions like `dropna()` or `fillna()`, the changes are not automatically saved. To save the updated DataFrame, you need to assign it back to the original variable or create a new variable with the updated data.
For example, if you use the `inplace=True` parameter when calling `dropna()`, the changes will be made directly to the original DataFrame and can be accessed without needing to reassign it. However, if you don't specify this parameter, the function will return a new DataFrame with the updated values, which you need to assign back to the original variable.
To illustrate this, let's consider an example where we want to use `inplace=True` when calling `dropna()`. We can do so by modifying the following code:
```python
import pandas as pd
# create a sample DataFrame
df = pd.DataFrame({
'role': ['1', '2', '3', '4', '5', '6', '7', '8'],
'species': ['A', None, 'C', 'D', 'E', 'F', 'G', 'H']
})
# drop rows with missing values
df.dropna(inplace=True)
print(df)
```
In this example, we use the `inplace=True` parameter when calling `dropna()`, which means that the changes will be made directly to the original DataFrame. As a result, when we print the resulting DataFrame, we can see that it has been updated correctly.
On the other hand, if we don't specify the `inplace=True` parameter, the function will return a new DataFrame with the updated values, which we need to assign back to the original variable:
```python
import pandas as pd
# create a sample DataFrame
df = pd.DataFrame({
'role': ['1', '2', '3', '4', '5', '6', '7', '8'],
'species': ['A', None, 'C', 'D', 'E', 'F', 'G', 'H']
})
# drop rows with missing values
new_df = df.dropna()
print(new_df)
```
In this example, we assign the result of `dropna()` to a new variable called `new_df`. As a result, when we print `new_df`, we can see that it has been updated correctly.
**Conclusion**
Dropping missing data is an essential step in handling missing values in pandas DataFrames. By using functions like `dropna()`, you can easily remove rows or columns that contain missing values from your DataFrame. Additionally, replacing missing values with new values can be useful if you want to impute missing values in a specific way. To save the updated DataFrame, you need to assign it back to the original variable or create a new variable with the updated data.