There are several ways to get the distribution of a column in Python, depending on the type of data and the desired output. Here are a few common methods:

- Using the
`pandas`

library: If the data is in a DataFrame, the`pandas`

library offers several methods to quickly get the distribution of a column, such as`value_counts()`

,`describe()`

, and`hist()`

. For example:

```
import pandas as pd
df = pd.read_csv('freshers_data.csv')
# To get the frequency count of each unique value in the column 'column_name'
df['column_name'].value_counts()
# To get summary statistics of the column
df['column_name'].describe()
# To plot a histogram of the column
df['column_name'].hist()
```

- Using the
`numpy`

library: If the data is in a numpy array, the`numpy`

library offers several methods to get the distribution of a column, such as`unique()`

,`count_nonzero()`

,`mean()`

,`std()`

. For example:

```
import numpy as np
data = np.genfromtxt('freshers_data.csv', delimiter=',')
# To get the unique values in the column
np.unique(data[:, column_index])
# To get the count of each unique value in the column
(values,counts) = np.unique(data[:, column_index], return_counts=True)
# To get the mean of the column
np.mean(data[:, column_index])
# To get the standard deviation of the column
np.std(data[:, column_index])
```

- Using the
`matplotlib`

library: The`matplotlib`

library can be used to plot a histogram of the column. For example:

```
import matplotlib.pyplot as plt
plt.hist(data[:, column_index])
plt.show()
```

- Using the
`seaborn`

library: The`seaborn`

library can be used to plot the distribution of the column. For example:

```
import seaborn as sns
sns.distplot(data[:, column_index])
plt.show()
```