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Now we will explore the dataset. Here, we will be using the `hist`

method to plot a histogram to view the data. A histogram is used to visually represent the distribution of the data instead of the actual data itself, simply put, it is used to summarize discrete or continuous data.

The `hist`

methods here has the `bins`

parameter. These are also sometimes referred to as `classes`

, `intervals`

, or `buckets, are groups of equal widths into which the data is separated. Each bin is plotted as a bar whose height corresponds to how many data points are in that bin.

We will also be using the `cut`

method from Pandas. This is used to segment and sort data values into bins. `cut`

is also helpful for converting from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. The `labels`

parameters here specifies the labels for the returned bins. It has to be of the same length as the resulting bins. Also, if you notice, we have mentioned a `np.inf`

here for the bins. That is a form of floating point representation of infinity.

Use the

`info`

method to get more information on the dataset`housing.<<your code goes here>>`

Get a better understand of the mean, standard deviation, maximum value and other such information from the dataset by using the

`describe`

method`housing.<<your code goes here>>`

Plot histograms of all the features using

`hist`

method`housing.<<your code goes here>>(bins=50, figsize=(20,15)) plt.show()`

Plot a histogram of the median income attribute of the dataset

`housing["median_income"].<<your code goes here>>`

Divide the median income attribute into bins and labels using the

`cut`

mthod, and then plot another histogram of the same`housing["income_cat"] = pd.<<your code goes here>>(housing["median_income"], bins=[0., 1.5, 3.0, 4.5, 6., np.inf], labels=[1, 2, 3, 4, 5]) housing["income_cat"].hist()`

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