P#16 Line, Bar, Scatter, Pie charts

MATPLOTLIB...

In this blog, we will see how to plot various chart types using plt.line(), plt.bar(), plt.barh(). plt.scatter(), plt.hist(). 

If you run this code which is self explanatory, you will get this chart. 

def plotline():
    import matplotlib.pyplot as plt
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.plot(langs, students)  # plot
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('line.png')
    plt.show()

plotline()

If you run this code, you will get this chart. 

def plotbar():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.bar(langs, students, col)  # BAR
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('bar.png')
    plt.show()

plotbar()

def plotbarh():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.barh(langs, students, color = 'hotpink')  # BARh Pink
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('bar.png')
    plt.show()

plotbarh() plots horizontal bar as shown below.

def plotbarss():
    import numpy as np
    import matplotlib.pyplot as plt
    data = [[30, 25, 50, 20],
            [40, 23, 51, 17],
            [35, 22, 45, 19]]
    X = np.arange(4)
    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1])
    ax.bar(X + 0.00, data[0], color='b', width=0.25)
    ax.bar(X + 0.25, data[1], color='g', width=0.25)
    ax.bar(X + 0.50, data[2], color='r', width=0.25)
    ax.legend(labels=('cse', 'it', 'mech', 'mba'), loc='upper right')
    plt.savefig('bars.png')
    plt.show()

plotbarss() displays the chart as shown below with three bars.

def plotscat():
    langs = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    plt.scatter(langs, students)  # Scatter
    plt.xlabel('Degree')
    plt.ylabel('Strength')
    plt.grid()
    plt.savefig('scat.png')
    plt.show()

plotscat()

plt.scatter() wil plot scatter plot as shown below()

def plotscatc():
    np.random.seed(19680801)  # seed the random number generator.
    data = {'a': np.arange(50),
            'c': np.random.randint(0, 50, 50),
            'd': np.random.randn(50)}
    data['b'] = data['a'] + 10 * np.random.randn(50)
    data['d'] = np.abs(data['d']) * 100

    fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
    ax.scatter('a', 'b', c='c', s='d', data=data, marker='*')
    ax.set_xlabel('entry a')
    ax.set_ylabel('entry b');
    plt.savefig('scatcc.png')
    plt.show()

plotscatc()

This will print different size, different colors.

This will print marker different size, different colors. 
marker = “*” sets the plot symbol as *. s = different size of the marker symbol in the plot. 

def plothist():
    import matplotlib.pyplot as plt
    import numpy as np
    x = np.random.normal(170, 10, 250)
    plt.hist(x)
    plt.savefig('hist.png')
    plt.show()

plt.hist() method plots histogram as shown below;

def plotpie():
    import matplotlib.pyplot as plt
    import numpy as np
    y = np.array([32, 40, 60, 80, 100])
    plt.pie(y)
    plt.savefig('pie.png')
    plt.show()

plotpie()

plot.pie() method to plot y.
def plotpie1():
    deg = ['B.E CSE', 'B.E. Marine', 'B.E. ECE', 'B.Sc(NatSci)', 'MBA']
    students = [20, 40, 60, 80, 100]
    # Creating plot
    fig = plt.figure(figsize=(8, 6))
    plt.pie(students, labels=deg)
    # show plot
    plt.savefig('pie1.png')
    plt.show()
plotpie1()

This is pie plot with labels. That is provide by  plt.pie(students, labels=deg) 

Happy learning with AMET ODL!!! 

Plotting #15

MATPLOTLIB

Matplotlib is a low level graph plotting library in python that serves as a visualization utility.

Matplotlib was created by John D. Hunter.

Matplotlib is open source and we can use it freely.

Matplotlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility.

Matplotlib என்பது பைத்தானில் உள்ள ஒரு குறைந்த அளவிலான வரைபடத் திட்டமிடல் நூலகமாகும், இது காட்சிப்படுத்தல் பயன்பாடாக செயல்படுகிறது.

Matplotlib ஜான் டி. ஹண்டர் என்பவரால் உருவாக்கப்பட்டது.

Matplotlib திறந்த மூலமாகும், அதை நாம் சுதந்திரமாகப் பயன்படுத்தலாம்.

Matplotlib பெரும்பாலும் python இல் எழுதப்பட்டுள்ளது, ஒரு சில பிரிவுகள் C, Objective-C மற்றும் Javascript இல் பிளாட்ஃபார்ம் இணக்கத்தன்மைக்காக எழுதப்பட்டுள்ளன.

import matplotlib.pyplot as plt
import numpy as np

x  = [1, 2, 3, 4, 5 ]
ls = [2, 4, 6, 8, 10]
d  = [0, 1, 1, 2, 2 ]

def plot1():
    plt.plot(x,ls)
    plt.title('1. First Graph')  ###
    plt.savefig('v31.png')
    plt.show()

plot1()

Simple definition for x, ls as list. Normally matplotlib is extensively used with np. 

Here we take minimum lists x, ls for plotting for easy understanding. 

Very simple import matplotlib, plot() with two declared lists, and show.

If u want you can save using savefig() method. This is written as function using def. 

You can simply call by plot1()

def plot2():
    plt.plot(x,ls,'r-')
    plt.title('2. Graph with Axes Labels and red')
    plt.xlabel('X-Axis')  ##
    plt.ylabel('Y-Axis')  ##
    plt.savefig('v32.png')
    plt.show()

plot2()

 The above plot2() will add title title and axis using plt.title() and plt.xlabel() and 

plt.ylabel() methods as shown above. see the result as below.

def plot3():
    plt.plot(x,ls,'g-')
    plt.title('3. Graph with Axes Labels + Grid Lines + Green')
    plt.xlabel('X-Axis')
    plt.ylabel('Y-Axis')
    plt.grid() #####
    plt.savefig('v33.png')
    plt.show()

plot3()

The above code uses plt.axis() method to add axis in the graph. The output will be similar to 

the one shown below:

def plot4():
    fig = plt.figure(figsize=(5, 4))
    plt.plot(x,ls,'r-')
    plt.title('4. Check the Figure Size ')
    plt.xlabel('X-Axis')
    plt.ylabel('Y-Axis')
    plt.grid()
    plt.savefig('v34.png')
    plt.show()

plot4()

figure size is defined by fig = plt.figure(figsize=(5, 4)). Check he effect of this statement 

as shown below. 

def plot5():
    fig = plt.figure(figsize=(5, 4))
    plt.plot(x, x,'b-', x, ls,'r-', x, d, 'g-')
    plt.title('5. Multiple Plots ')
    plt.xlabel('X-Axis')
    plt.ylabel('Y-Axis')
    plt.grid()
    plt.savefig('v35.png')
    plt.show()

plot5()

See the multiple data can be plotted in a single graphs by the plt.plot(x, x,'b-', x, ls,'r-', x, d, 'g-')

Please notice the 'b-' to denote blue normal line, 'g-' to denote the green line. See the result:

def plot6():
    # using numparray
    fig = plt.figure(figsize=(5, 4))
    x =  np.array([1, 2, 3, 4, 5])
    ls = np.array([2, 4, 6, 8, 10])
    d =  np.array([0, 1, 1, 2, 2])
    plt.plot(x, x,'b-')
    plt.plot(x, ls,'r-')
    plt.plot(x, d, 'g-')
    plt.title('6. Multiple Plots  using np array')
    plt.xlabel('Quantity')
    plt.ylabel('Price')
    plt.grid()
    plt.savefig('v36.png')
    plt.show()

plot6()

This code snippet is another way, used separate plt.plot() statements. Also data set is defined as num arrays.

See the result graph as shown below.

def plot7():
    x = np.array([1, 2, 3, 4, 5])
    ls = np.array([2, 4, 6, 8, 10])
    d = np.array([0, 1, 1, 2, 2])

    plt.title('6. MultipleSub  Plots  Column wise')
    # plot 1:
    plt.subplot(1, 2, 1)
    plt.plot(x, x)
    plt.title('Qty Vs Qty')

    # plot 2:
    plt.subplot(1, 2, 2)
    plt.plot(x, ls)
    plt.title('Qty Vs Price')
    plt.savefig('v37.png')
    plt.show()

plot7()

This plot explains the sub plots. plt.subplot(1, 2, 1) indicates the graph has 1 row, 2 columns, and this 

plot is the first plot. plt.supplot(1, 2, 2) indicates figure has 1 row, 2 columns, and this plot is the

second plot. See the graph below to depict the above code snippet.

def plot8():
    x = np.array([1, 2, 3, 4, 5])
    ls = np.array([2, 4, 6, 8, 10])
    d = np.array([0, 1, 1, 2, 2])
    plt.title('6. Sub Plots  Row wise using np array')

    # plot 1:
    plt.subplot(2, 2, 1)
    plt.plot(x, x)
    plt.title('Qty Vs Qty')

    # plot 2:
    plt.subplot(2, 2, 2)
    plt.plot(x, ls)
    plt.title('Quantity Vs Price')
    plt.savefig('v38.png')
    plt.show()

plot8()

The output graph is given above side by side  to compare between the snippets

plt.subplot(1, 2, 1),  plt.supplot(1, 2, 2) Vs plt.subplot(2, 2, 1) and plt.subplot(2, 2, 2)
Happy learning with AMET ODL🙋🙋🙋!!!

Python Visualizing λ

λ +

Python compact coding is very much possible with lambda function.

Anonymous function.. Function without name. 

# lambda arguments : expression

# using Lambda
result = lambda x,y : x + y
# print(result(5,5))

# otherwise by function
def add(x,y):
    return (x+y)
add(5,5)

sorted() takes elements from the list and sort the elements. Please see how key is implemented using lambda function. This can be used for any list with numbers, strings, list of lists. For simple understanding, number list is given. keys are defined using lambda arguments.

# Difference Between function and lambda -anonymous function
def cube(x):
    return (x*x*x)

lambda_cube = lambda x: x*x*x

print(cube(125))
print((lambda_cube(25))) # No function call  or return

list_of_tuples = [(1, 2, 3, 4), (4, -3, 2, 1), (3, 4, -1, 2), (2, 3, 4, -1)]
list_of_tuples_sorted = sorted(list_of_tuples)
print(list_of_tuples_sorted)
"""
[(1, 2, 3, 4), (2, 3, 4, -1), (3, 4, -1, 2), (4, -3, 2, 1)]
"""

list_of_tuples_sorted = sorted(list_of_tuples, key =lambda x: x[1])  # Sort in second element of all tuples
print(list_of_tuples_sorted)
"""
[(4, -3, 2, 1), (1, 2, 3, 4), (2, 3, 4, -1), (3, 4, -1, 2)]
"""

list_of_tuples_sorted = sorted(list_of_tuples, key =lambda x: x[3])  # Sort in last element of all tuples
print(list_of_tuples_sorted)

"""
[(2, 3, 4, -1), (4, -3, 2, 1), (3, 4, -1, 2), (1, 2, 3, 4)] """

Map function is used to pass the arguments from the list. Literally mapping values..

# Using function map()
alist = [1, 2, 3, 4, 5]
mlist = map(lambda x : x*x, alist)  # map supplies elements instead of one element
print(list(mlist)) # [1, 4, 9, 16, 25]

# Otherwise using c-omprehensive list
clist = [x * x for x in alist]
print(clist) # [1, 4, 9, 16, 25]

#Using Filter to find even numbers in the list

flist = filter(lambda x: x%2 == 0, alist)
print(list(flist)) # [2, 4]

# Otherwise using c-omprehensive list
clist = [x for x in alist if x%2== 0]
print(clist) # [2, 4]

#Using reduce ....please import reduce from functools

def my_add(a, b):
   result = a + b
   print(f"{a} + {b} = {result}")
   return result

from functools import reduce
numbers = [0, 1, 2, 3, 4]
print(reduce(my_add, numbers))  
# Note passing a,b  and  see the cumulative sum is first  
element in the next iteration
0 + 1 = 1
1 + 2 = 3
3 + 3 = 6
6 + 4 = 10
10
"""
import functools

# list definition
alist = [1, 2, 7, 4, 5, 6, 3, ]

# educe to compute sum of list
print("The sum of the list elements is : ", end="")
print(functools.reduce(lambda a, b: a + b, alist))   #28

# reduce to compute maximum element from list
print("The maximum element of the list is : ", end="")
print(functools.reduce(lambda a, b: a if a > b else b, alist))  # 7

To understand the visualization the above reduce code execution: please click the  link https://pythontutor.com/visualize.html#mode=display Paste the above code in the editor window. Click visualize execution and next button. 

Step 1:

Step 2: Delete the first line (if any) as shown in the above screen shot. 

Paste the copied code as shown below

Step 3: Click Visualize Button to see the screen shot similar to the following one:

Step 4: Click Next Button to see the following 

Please note global frame with functools points with arrow to  objects module instance by arrow.

Step 5: Click Next button again to see as shown below

Notice the pointer from alist  1,2,7,4,5.  

Step 5: Click again to see the window similar to one shown below:

Please note the print out box in top right : First print out put 

'The sum of the list elements is seen  :'

Step 6:  Again Click Next  to see the window as shown below.

In this screen shot, please notice  lambda list with a =1 ,b=2 . Press Clicks to see the screen below to see the return value 3:

Step 7:.. Click Next many times until that button is disabled  to see execution until you get results ' The maximum element of the list is : 7  as shown in the screen below.

This is the video showing the execution.

Hope, you understand the execution of the python code. 

Hey, you know how to write concise coding using Lambda.. If you don't, sorry, God only save you!!!!😇

Happy Learning with AMET, ODL

Pandas#02

 PANDAS .... (Analysing)

# Analyzing Data Frames

print(df.info())
"""
Int64Index: 169 entries, 0 to 168
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Duration  169 non-null    int64  
 1   Pulse     169 non-null    int64  
 2   Maxpulse  169 non-null    int64  
 3   Calories  164 non-null    float64
dtypes: float64(1), int64(3)
memory usage: 6.6 KB
None
"""

The info() method also tells 

• Non-Null values there are present in each column, (164 of 169 Non-Null values in the "Calories" column.) . 

• 5 rows with no value at all, in the "Calories" column, for whatever reason.

• Empty values, or Null values, can be bad when analyzing data, duplicate values and 

• Removing rows with empty values  called cleaning data, and you will learn more about that in the next chapters.

Let us dive in to the code for cleaning. Assume the data file as dirtydata.csv

import pandas as pd
df = pd.read_csv('dirtydata.csv') # reading dirty
# print(df)
"""
    Duration          Date  Pulse  Maxpulse  Calories
0         60  '2020/12/01'    110       130     409.1
1         60  '2020/12/02'    117       145     479.0
2         60  '2020/12/03'    103       135     340.0
3         45  '2020/12/04'    109       175     282.4
4         45  '2020/12/05'    117       148     406.0
5         60  '2020/12/06'    102       127     300.0
6         60  '2020/12/07'    110       136     374.0
7        450  '2020/12/08'    104       134     253.3
8         30  '2020/12/09'    109       133     195.1
9         60  '2020/12/10'     98       124     269.0
10        60  '2020/12/11'    103       147     329.3
11        60  '2020/12/12'    100       120     250.7
12        60  '2020/12/12'    100       120     250.7
13        60  '2020/12/13'    106       128     345.3
14        60  '2020/12/14'    104       132     379.3
15        60  '2020/12/15'     98       123     275.0
16        60  '2020/12/16'     98       120     215.2
17        60  '2020/12/17'    100       120     300.0
18        45  '2020/12/18'     90       112       NaN
19        60  '2020/12/19'    103       123     323.0
20        45  '2020/12/20'     97       125     243.0
21        60  '2020/12/21'    108       131     364.2
22        45           NaN    100       119     282.0
23        60  '2020/12/23'    130       101     300.0
24        45  '2020/12/24'    105       132     246.0
25        60  '2020/12/25'    102       126     334.5
26        60      20201226    100       120     250.0
27        60  '2020/12/27'     92       118     241.0
28        60  '2020/12/28'    103       132       NaN
29        60  '2020/12/29'    100       132     280.0
30        60  '2020/12/30'    102       129     380.3
31        60  '2020/12/31'     92       115     243.0
"""
print(df.info)
"""
<bound method DataFrame.info of     Duration          Date  Pulse  Maxpulse  Calories
0         60  '2020/12/01'    110       130     409.1
1         60  '2020/12/02'    117       145     479.0
2         60  '2020/12/03'    103       135     340.0
3         45  '2020/12/04'    109       175     282.4
4         45  '2020/12/05'    117       148     406.0
5         60  '2020/12/06'    102       127     300.0
6         60  '2020/12/07'    110       136     374.0
7        450  '2020/12/08'    104       134     253.3
8         30  '2020/12/09'    109       133     195.1
9         60  '2020/12/10'     98       124     269.0
10        60  '2020/12/11'    103       147     329.3
11        60  '2020/12/12'    100       120     250.7
12        60  '2020/12/12'    100       120     250.7
13        60  '2020/12/13'    106       128     345.3
14        60  '2020/12/14'    104       132     379.3
15        60  '2020/12/15'     98       123     275.0
16        60  '2020/12/16'     98       120     215.2
17        60  '2020/12/17'    100       120     300.0
19        60  '2020/12/19'    103       123     323.0
20        45  '2020/12/20'     97       125     243.0
21        60  '2020/12/21'    108       131     364.2
23        60  '2020/12/23'    130       101     300.0
24        45  '2020/12/24'    105       132     246.0
25        60  '2020/12/25'    102       126     334.5
26        60      20201226    100       120     250.0
27        60  '2020/12/27'     92       118     241.0
29        60  '2020/12/29'    100       132     280.0
30        60  '2020/12/30'    102       129     380.3
31        60  '2020/12/31'     92       115     243.0>
"""
df.dropna(inplace = True)
# print(df.to_string())
print(df) # check no values are dropped
"""
   Duration          Date  Pulse  Maxpulse  Calories
0         60  '2020/12/01'    110       130     409.1
1         60  '2020/12/02'    117       145     479.0
2         60  '2020/12/03'    103       135     340.0
3         45  '2020/12/04'    109       175     282.4
4         45  '2020/12/05'    117       148     406.0
5         60  '2020/12/06'    102       127     300.0
6         60  '2020/12/07'    110       136     374.0
7        450  '2020/12/08'    104       134     253.3
8         30  '2020/12/09'    109       133     195.1
9         60  '2020/12/10'     98       124     269.0
10        60  '2020/12/11'    103       147     329.3
11        60  '2020/12/12'    100       120     250.7
12        60  '2020/12/12'    100       120     250.7
13        60  '2020/12/13'    106       128     345.3
14        60  '2020/12/14'    104       132     379.3
15        60  '2020/12/15'     98       123     275.0
16        60  '2020/12/16'     98       120     215.2
17        60  '2020/12/17'    100       120     300.0
19        60  '2020/12/19'    103       123     323.0
20        45  '2020/12/20'     97       125     243.0
21        60  '2020/12/21'    108       131     364.2
23        60  '2020/12/23'    130       101     300.0
24        45  '2020/12/24'    105       132     246.0
25        60  '2020/12/25'    102       126     334.5
26        60      20201226    100       120     250.0
27        60  '2020/12/27'     92       118     241.0
29        60  '2020/12/29'    100       132     280.0
30        60  '2020/12/30'    102       129     380.3
31        60  '2020/12/31'     92       115     243.0
"""

See the last result output doesn't have NaN values. inplace = True, it will clean in the same place.

dropna() will delete the rows with NaN.