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mlxtend Machine Learning Extensions, a Python library of useful tools for the day-to-day data science tasks. - TransactionEncoder We can transform this dataset into an array format suitable for typical machine learning APIs. The NumPy array is boolean for the sake of memory efficiency when working with large datasets. fit : the TransactionEncoder learns the unique labels in the dataset transform..

read_csv - sep : Separator - names : List of column names to use. - header : To use as the column names, and the start of the data. If column names are passed explicitly then the behavior is identical to header=None. - low_memory : It results mixed type inference, so set False to ensure no mixed type. columns=['user_id', 'item_id', 'rating', 'timestamp'] df=pd.read_csv('./u.data',sep='\t', names..

array Create an array. dtype="object" a_object = np.array([1, 0.1, 'one'], dtype=object) print(a_object) print(a_object.dtype) >>> [1 0.1 'one'] >>> object print(type(a_object[0])) print(type(a_object[1])) print(type(a_object[2])) >>> >>> >>> It stores pointers to Python objects. Since the data of each element itself is stored in each memory area, it can have multiple data types (pointers to) in..

2. DataFrame Two-dimensional, added columns to Series. df=pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]],index=['one','two','three'],columns=['a','b','c']) df >>>abc one123 two456 three789 df.values >>>array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) df.index >>>Index(['one', 'two', 'three'], dtype='object') df.columns >>>Index(['a', 'b', 'c'], dtype='object') - Create DataFrame with list data=[['1000', 'Steve'..

pandas It is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. 1. Series One-dimensional array with values and index can be granted to each values. import pandas as pd sr=pd.Series([1000,2000,3000,4000],index=['aaa','bbb','ccc','ddd']) sr >>>..