Python Data Analysis
A common task for scientists and engineers is to analyze data from an external source that may be in a text or comma separated value (CSV) format. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. This tutorial demonstrates how to import data, perform a basic analysis, trend the results, and export the results to another text file. Two examples are provided with Numpy and Pandas. Script files of the Python source code with sample data are below.
Import Data and Analyze with Numpy
Import Data and Analyze with Pandas
# import Numpy, Pandas, and Matplotlib import numpy as np import pandas as pd import matplotlib.pyplot as plt # load the data file data_file = pd.read_csv('data_with_headers.csv') # create time vector from imported data time = data_file['time'] # parse good sensor data from imported data sensors = data_file.ix[:,'s1':'s4'] # display the first 6 sensor rows print(sensors[0:6]) # adjust time to start at zero by subtracting the # first element in the time vector (index = 0) time = time - time # calculate the average of the sensor readings avg = np.mean(sensors,1) # over the 2nd dimension # export data my_data = [time, sensors, avg] result = pd.concat(my_data,axis=1) result.to_csv('result.csv') #result.to_excel('result.xlsx') result.to_html('result.htm') result.to_clipboard() # generate a figure plt.figure(1) plt.plot(time,sensors['s1'],'r-') plt.plot(time,avg,'b.') # add text labels to the plot plt.legend(['Sensor 2','Average']) plt.xlabel('Time (sec)') plt.ylabel('Sensor Values') # save the figure as a PNG file plt.savefig('my_Python_plot.png') # show the figure on the screen plt.show()
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