Coding Ref

To calculate the standard deviation for a column in a Pandas DataFrame, you can use the `std`

method.

This method is applied to a `Series`

object and returns the standard deviation 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 standard deviation 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 standard deviation for column B
std = df['B'].std()
# Print the resulting value
print(std)
```

output

```
15.811388300841896
```

In the code above, the `std`

method is applied to the `B`

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

In this case, the resulting standard deviation is 15.811388300841896.

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

method.

For example, if you wanted to calculate the standard deviation 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 standard deviation for columns B and C
std = df[['B', 'C']].std()
# Print the resulting Series
print(std)
```

output

```
B 15.811388
C 158.113883
dtype: float64
```

In the code above, the `std`

method is applied to the `B`

and `C`

columns of the DataFrame, which calculates the standard deviation for the elements in those columns.

The result is a new `Series`

object containing the standard deviation for each column.

You can also use the `std`

method to calculate the standard deviation for the entire DataFrame.

To do this, you can use the `apply`

method in combination with the `std`

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 standard deviation for the entire DataFrame
std = df.apply(lambda x: x.std())
# Print the resulting Series
print(std)
```

output

```
A 1.581139
B 15.811388
C 158.113883
dtype: float64
```

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