Pandas and matplotlib

matplotlibI have used R Data Frames and they were very versatile and compared to that the pandas Data Frames seem slightly harder to get right. But I am after the excellent support for Machine Learning and data analytics that scikit provides.

This graph is simple and I usually parse Java GC logs to practise. I plan to parse the Java G1 GC log to get my hands dirty by using pandas Data Frames.

  AfterSize BeforeSize RealTime       SecondsSinceLaunch TotalSize
0        20      3.109     9216  2014-05-13T13:24:35.091      5029
1      9125      3.459     9216  2014-05-13T13:24:35.440      6077
2        25      5.599     9216  2014-05-13T13:24:37.581      8470
3        44     10.704     9216  2014-05-13T13:24:42.686        15
4        51     16.958     9216  2014-05-13T13:24:48.941        20
5        92     24.066     9216  2014-05-13T13:24:56.049        26
6       602     62.383     9216  2014-05-13T13:25:34.368        68
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

def main():
    gclog = pd.DataFrame(columns=['SecondsSinceLaunch',
                                   'BeforeSize',
                                   'AfterSize',
                                   'TotalSize',
                                   'RealTime'])
    with open("D:\\performance\\data.txt", "r") as f:
        for line in f:
            strippeddata = line.split()
            gclog = gclog.append(pd.DataFrame( [dict(SecondsSinceLaunch=strippeddata[0],
                                                     BeforeSize=strippeddata[1],
                                                     AfterSize=strippeddata[2],
                                                     TotalSize=strippeddata[3],
                                                     RealTime=strippeddata[4])] ),
                                               ignore_index=True)
    print gclog
    #gclog.time = pd.to_datetime(gclog['SecondsSinceLaunch'], format='%Y-%m-%dT%H:%M:%S.%f')
    gclog = gclog.convert_objects(convert_numeric=True)
    plt.plot(gclog.TotalSize, gclog.AfterSize)
    plt.show()
if __name__=="__main__":
    main()

matplotlib

Update :

The graph shown above is not clear and it looks wrong. I have improved it to some extent using this code. Matplotlib has many features more powerful than what I used earlier. I have commented the code used to annotate and display the actual points in the graph. I couldn’t properly draw the tick marks so that the red graph is clearly shown because the data range wasn’t easy to work with. There should be some feature that I still have not explored.


import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

def main():
    gclog = pd.DataFrame(columns=['SecondsSinceLaunch',
                                   'BeforeSize',
                                   'AfterSize',
                                   'TotalSize',
                                   'RealTime'])
    with open("D:\\performance\\data.txt", "r") as f:
        for line in f:
            strippeddata = line.split()
            gclog = gclog.append(pd.DataFrame( [dict(SecondsSinceLaunch=strippeddata[0],
                                                     BeforeSize=strippeddata[1],
                                                     AfterSize=strippeddata[2],
                                                     TotalSize=strippeddata[3],
                                                     RealTime=strippeddata[4])] ),
                                               ignore_index=True)
    print gclog
    #gclog.time = pd.to_datetime(gclog['SecondsSinceLaunch'], format='%Y-%m-%dT%H:%M:%S.%f')
    gclog = gclog.convert_objects(convert_numeric=True)
    fig, ax = plt.subplots(figsize=(17, 14), facecolor='white', edgecolor='white')
    ax.axes.tick_params(labelcolor='darkblue', labelsize='10')
    for axis, ticks in [(ax.get_xaxis(), np.arange(10, 8470, 100) ), (ax.get_yaxis(), np.arange(10, 9125, 300))]:
        axis.set_ticks_position('none')
        axis.set_ticks(ticks)
        axis.label.set_color('#999999')
        if False: axis.set_ticklabels([])
    plt.grid(color='#999999', linewidth=1.0, linestyle='-')
    plt.xticks(rotation=70)
    plt.gcf().subplots_adjust(bottom=0.15)
    map(lambda position: ax.spines[position].set_visible(False), ['bottom', 'top', 'left', 'right'])
    ax.set_xlabel(r'AfterSize'), ax.set_ylabel(r'TotalSize')
    ax.set_xlim(10, 8470, 100), ax.set_ylim(10, 9125, 300)    
    plt.plot(sorted(gclog.AfterSize),gclog.TotalSize,c="red")
#     for i,j in zip(sorted(gclog.AfterSize),gclog.TotalSize):
#         ax.annotate('(' + str(i) + ',' + str(j) + ')',xy=(i, j))
    
    plt.show()
if __name__=="__main__":
    main()

figure_1

3 Responses to Pandas and matplotlib

  1. Pingback: Aesthetics of Matplotlib graphs | MindSpace

  2. Pingback: Parsing Java Micro-benchmarking Harness data using dplyr – Part 2 | MindSpace

  3. What’s up, this weekend is fastidious in support of me,
    for the reason that this moment i am reading this enormous educational paragraph
    here at my house.

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