Coding Ref

To calculate the variance for a column in a Pandas DataFrame, you can use the `var`

method.

This method is applied to a `Series`

object and returns the variance for the elements in that `Series`

.

For example, consider the following DataFrame:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
```

This DataFrame has three columns `A`

, `B`

, and `C`

, with five rows of data.

To calculate the variance for a specific column, you could do the following:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Calculate the variance for column B
variance = df['B'].var()
# Print the resulting value
print(variance)
```

In the code above, the `var`

method is applied to the `B`

column of the DataFrame, which calculates the variance for the elements in that column.

In this case, the resulting variance is 250.

You can also specify multiple columns when using the `var`

method.

For example, if you wanted to calculate the variance for both columns `B`

and `C`

, you could do the following:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Calculate the variance for columns B and C
variance = df[['B', 'C']].var()
# Print the resulting Series
print(variance)
```

output

```
B 250.0
C 25000.0
dtype: float64
```

In the code above, the `var`

method is applied to the `B`

and `C`

columns of the DataFrame, which calculates the variance for the elements in those columns. The result is a new `Series`

object containing the variance for each column.

You can also use the `var`

method to calculate the variance for the entire DataFrame.

To do this, you can use the `apply`

method in combination with the `var`

method, as shown in the following example:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Calculate the variance for the entire DataFrame
variance = df.apply(lambda x: x.var())
# Print the resulting Series
print(variance)
```

output

```
A 2.5
B 250.0
C 25000.0
dtype: float64
```

In the code above, the `apply`

method is used to apply the `var`

method to each column in the DataFrame.

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