# Ways to get the distribution of a column in Python, depending on the type of data

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:

1. 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
# 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()
``````
1. 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])
``````
1. 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()
``````
1. 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()
``````
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