Example Analysis of Pandas data Storage

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Storage of data

There can be two types of data-continuous and discrete, depending on our analysis requirements. Sometimes we don't need the exact value in a continuous variable, but we need the group to which it belongs.

For example, you have a continuous variable in your data, age. But you need an age group to analyze, such as children, teenagers, adults, the elderly. In fact, Binning is very suitable for solving our problems here.

To execute Binning, we use the cut () function. This is useful for moving from continuous variables to discrete variables.

Import pandas as pddf = pd.read_csv ('titanic.csv') from sklearn.utils import shuffle# Randomized df = shuffle (df, random_state = 42) df.head () bins = [0LJ 4, random_state = 42) df.head () bins = [0Toddler','Child','Adult','Elderly'] category = pd.cut (df [' Age'], bins = bins, labels = labels) df.insert (2, 'Age Group') Category) df.head () df ['Age Group']. Value_counts () df.isnull (). Sum () these are all the contents of the article "sample Analysis of Pandas data Storage" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!