Showing posts with the label Python Text Files

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The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

How to Read CSV file Data in Python

Here is a way to read  CSV files  in Python pandas. The packages you need to import are numpy and pandas. On the flip side, f or Text files, you don't need to import these special libraries since python by default support it. Python pandas read_csv >>> import numpy as np >>> import pandas as pd To see how pandas handle this kind of data, we'll create a small CSV file in the working directory as ch05_01.csv. white, red, blue, green, animal 1,5,2,3,cat  2,7,8,5,dog  3,3,6,7,horse  2,2,8,3,duck  4,4,2,1,mouse Since this file is comma-delimited , you can use the read_csv() function to read its content and convert it to a dataframe object. >>> csvframe = pd.read_csv('ch05_01.csv') >>> csvframe white red blue green animal 0 1 5 2 3 cat 1 2 7 8 5 dog 2 3 3 6 7 horse 3 2 2 8 3 duck 4 4 4 2 1 mouse Python reading text files Since python supp