Question

I am trying to make a scatter plot in Python. I supposed it will be fairly simple but got stuck with understanding in scatterplot (x and y value) while plotting.

==My mission ==

• I have database and more then 10k record (all float) till now and will increase on daily basis.
• The record range is from 200-2000 (in float decimal).
• So, I want to see the most populated region in my dataset.

==What I did?==

import numpy as np
import pylab as pl
import MySQLdb
import sys
import math

conn = MySQLdb.connect(
    host="localhost",
    user="root",
    passwd="root",
    db="myproject")

with conn:
    cur = conn.cursor()

    #will fetch all recoreds called monoiso field
    cur.execute("SELECT monoiso FROM pmass_selectedion")
    rows = cur.fetchall()

    for row in rows:

        #xvalue for monoiso variable and yvalue for range 
        xvalue = row
        yvalue = [600]

        # tried this way too but got x and y dimension error
        #yvalue = [400,800,1200,1600]

        pl.plot(xvalue,yvalue,'ro')
pl.show()

Scatterplot Understanding (link)

Ok! this plot doesnt make any sense.

==Question ==

• How to make scatter plot to see the most populated region?
• How can I assign y variable to make equal dimension with x variable(total number of fetched records)?

New to plotting and statistic so please help me out

Answer 1

Perhaps you are looking for a matplotlib histogram:

import numpy as np
import MySQLdb
import matplotlib.pyplot as plt # This is meant for scripts
# import pylab as pl # This is meant for interactive sessions; 
import operator

conn = MySQLdb.connect(
    host="localhost",
    user="root",
    passwd="root",
    db="myproject")

with conn:
    cur = conn.cursor()

    #will fetch all recoreds called monoiso field
    cur.execute("SELECT monoiso FROM pmass_selectedion")
    rows = cur.fetchall()

monoisos = [row[0] for row in rows]

# Make a histogram of `monoisos` with 50 bins.
n, bins, histpatches = plt.hist(monoisos, 50, facecolor = 'green')
plt.show()

---

You can also make a histogram/dot-plot by using numpy.histogram:

momoisos = [row[0] for row in rows]
hist, bin_edges = np.histogram(monoisos, bins = 50)
mid = (bin_edges[1:] + bin_edges[:-1])/2
plt.plot(mid, hist, 'o')
plt.show()

---

Regarding the use of pylab: The docstring for pyplot says

matplotlib.pylab combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it is
recommended that the namespaces be kept separate.