Add support for Chi-square distribution

USAGE.md

@@ -113,13 +113,13 @@
 
 # to calculate mean
 >>> print(exponential.calculate_mean(1))
-2.0 
+2.0
 
-# to calculate standard deviation 
+# to calculate standard deviation
 >>> print(exponential.calculate_stdev(1))
-2.0 
+2.0
 
-# to calculate pdf 
+# to calculate pdf
 >>> print(exponential.calculate_pdf(5, 5))
 0.04104
 
@@ -129,11 +129,12 @@
 >>> print(exponential.calculate_cdf(-2))
 0.0
 
-# plot pdf of exponential distribution 
->>> exponential.plot_bar_pdf() 
+# plot pdf of exponential distribution
+>>> exponential.plot_bar_pdf()
 ```
 
 ## For Gamma Distribution
+
 ```
 >>> from probdists import Gamma
 
@@ -185,33 +186,34 @@
 # The resulting gamma three will have k=3, theta=2.
 # This add magic method fails if thetas are not equal since they wouldn't be summable
 ```
-## For Uniform Distribution 
+
+## For Uniform Distribution
 
 ```
->>> from probdists import Uniform 
+>>> from probdists import Uniform
 
 # default value of the interval is (1,10)
 >>> uniform = Uniform()
 
 >>> uniform2 = Uniform(1, 5)
-# interval of uniform2 is (1,5) 
+# interval of uniform2 is (1,5)
 
 >>> uniform.read_data_file('demo_uniform_data')
 # pass in your filename to read data from filename
 
-# to access data 
+# to access data
 >>> print(uniform.data)
-[4,5,2,3,3,2,2,5,4,3,1,3,5,3,4] 
+[4,5,2,3,3,2,2,5,4,3,1,3,5,3,4]
 
 # to calculate mean
 >>> print(uniform.calculate_mean())
-5 
+5
 
-# to calculate standard deviation 
+# to calculate standard deviation
 >>> print(uniform.calculate_stdev())
-2.89 
+2.89
 
-# to calculate pdf 
+# to calculate pdf
 >>> print(uniform.calculate_pdf(5))
 0.1
 
@@ -225,10 +227,10 @@
 # plot histogram of data
 >>> uniform.plot_histogram()
 
-# plot pdf of uniform distribution 
->>> uniform.plot_bar_pdf() 
+# plot pdf of uniform distribution
+>>> uniform.plot_bar_pdf()
 
-# to calculate cdf 
+# to calculate cdf
 >>> uniform.replace_stats_with_data()
 >>> print(uniform.calculate_cdf(0))
 0
@@ -237,6 +239,7 @@
 >>> print(uniform.calculate_cdf(4))
 0.75
 ```
+
 ## For Bernoulli Distribution
 
 ```
@@ -271,9 +274,9 @@
 >>> print(bernoulli.calculate_pdf(1, 1))
 0.3
 
-# to calculate cdf 
+# to calculate cdf
 >>> print(bernoulli.calculate_cdf(0.7))
-0.7 
+0.7
 >>> print(bernoulli.calculate_cdf(2))
 1
 
@@ -297,6 +300,7 @@
 ```
 
 ## For Triangular Distribution
+
 ```
 >>> from probdists import Triangular
 
@@ -324,17 +328,29 @@
 >>> print(triangle.calculate_pdf(0.5))
 2
 
-# to calculate cdf 
+# to calculate cdf
 >>> print(triangle.calculate_cdf(0.5))
 0.5
 >>> print(triangle.calculate_cdf(1))
 1
 
-# to access data 
+# to access data
 >>> print(triangle.data)
 [1, 2, 3, 4, 5, 5, 6, 8, 9, 13]
 
-# plot pdf of triangular distribution 
->>> triangle.plot_bar_pdf() 
+# plot pdf of triangular distribution
+>>> triangle.plot_bar_pdf()
+
+```
 
+## For chi Square distribution
+
+```
+# importing Chi_squareDistribution
+>>>from probdists import Chi_squareDistribution
+>>>chi = Chi_squareDistribution()
+# to read data from csv
+>>>chi.read_data_file("filename.csv ")
+# to print chi Square value
+>>>chi.chi_square()
 ```

probdists/Bernoullidistribution.py

@@ -2,10 +2,11 @@
 import matplotlib.pyplot as plt
 from .Generaldistribution import Distribution
 from .Binomialdistribution import Binomial
+import seaborn as sns
 
 
 class Bernoulli(Distribution):
-    """ Bernoulli distribution class for calculating and
+    """Bernoulli distribution class for calculating and
     visualizing a Bernoulli distribution.
 
     Attributes:
@@ -23,7 +24,7 @@ def __init__(self, prob=0.5):
                               self.calculate_stdev())
 
     def calculate_mean(self, round_to=2):
-        """ Method to calculate the mean of a Bernoulli distribution
+        """Method to calculate the mean of a Bernoulli distribution
 
         Args:
             round_to (int): Round the mean value. Defaults to 2.
@@ -51,7 +52,7 @@ def calculate_stdev(self, round_to=2):
         return round(self.stdev, round_to)
 
     def replace_stats_with_data(self):
-        """ Method to calculate p from the data set
+        """Method to calculate p from the data set
 
         Args:
             None
@@ -67,8 +68,8 @@ def replace_stats_with_data(self):
         return self.p
 
     def plot_bar(self):
-        """ Method to plot a histogram of the instance variable data using
-        matplotlib pyplot library.
+        """Method to plot a histogram of the instance variable data using
+        seaborn pyplot library.
 
         Args:
             None
@@ -77,13 +78,12 @@ def plot_bar(self):
             None
         """
 
-        plt.bar(x=['0', '1'], height=[(1 - self.p), self.p])
-        plt.title('Bar Chart of Data')
-        plt.xlabel('outcome')
-        plt.ylabel('count')
+        sns.barplot(x=["0", "1"], height=[(1 - self.p), self.p]).set(
+            title="Bar Chart of Data", xlabel="outcome", ylabel="count"
+        )
 
     def calculate_pdf(self, k, round_to=2):
-        """ Method to calculate pdf for the bernoulli distribution.
+        """Method to calculate pdf for the bernoulli distribution.
 
         Args:
             k (float): point for calculating the probability density function. Range of k: {0,1}
@@ -102,7 +102,7 @@ def calculate_pdf(self, k, round_to=2):
         return round(self.pdf, round_to)
 
     def calculate_cdf(self, k, round_to=2):
-        """ Method to calculate cdf for the bernoulli distribution.
+        """Method to calculate cdf for the bernoulli distribution.
 
         Args:
             k (float): point for calculating the cumulative distribution function
@@ -112,7 +112,7 @@ def calculate_cdf(self, k, round_to=2):
             float: cumulative distribution function output
         """
 
-        val = 0                 # default value of cdf for k < 0
+        val = 0  # default value of cdf for k < 0
         if 0 <= k < 1:
             val = 1 - self.p
         elif k > 1:
@@ -121,7 +121,7 @@ def calculate_cdf(self, k, round_to=2):
         return round(self.cdf, round_to)
 
     def plot_bar_pdf(self):
-        """ Method to plot the pdf of the bernoulli distribution
+        """Method to plot the pdf of the bernoulli distribution
 
         Args:
             None
@@ -137,17 +137,15 @@ def plot_bar_pdf(self):
             y.append(self.pdf)
 
         # draw the plots
-        plt.bar(x, y)
-        plt.title("Distribution of Outcomes")
-        plt.ylabel("Probability")
-        plt.xlabel("Outcome")
-
+        plt.barplot(x, y).set(
+            title="Distribution of Outcomes", ylabel="Probability", xlabel="Outcome"
+        )
         plt.show()
 
         return x, y
 
     def __add__(self, other):
-        """ Method to add together two Bernoulli distributions with equal p
+        """Method to add together two Bernoulli distributions with equal p
 
         Args:
             other (Bernoulli): Bernoulli instance
@@ -157,7 +155,7 @@ def __add__(self, other):
         """
 
         try:
-            assert self.p == other.p, 'p values are not equal'
+            assert self.p == other.p, "p values are not equal"
         except AssertionError:
             raise
 
@@ -170,13 +168,15 @@ def __add__(self, other):
         return result
 
     def __repr__(self):
-        """ Method to output the characteristics of this Bernoulli instance
+        """Method to output the characteristics of this Bernoulli instance
         Args:
             None
 
         Returns:
             string: characteristics of this Bernoulli instance
         """
 
-        return 'mean {0}, standard deviation {1}, \
-                p {2}, q {3}'.format(self.mean, self.stdev, self.p, 1.0 - self.p)
+        return "mean {0}, standard deviation {1}, \
+                p {2}, q {3}".format(
+            self.mean, self.stdev, self.p, 1.0 - self.p
+        )

probdists/Binomialdistribution.py

@@ -1,10 +1,11 @@
 import math
 import matplotlib.pyplot as plt
 from .Generaldistribution import Distribution
+import seaborn as sns
 
 
 class Binomial(Distribution):
-    """ Binomial distribution class for calculating and
+    """Binomial distribution class for calculating and
     visualizing a Binomial distribution.
 
     Attributes:
@@ -15,13 +16,12 @@ class Binomial(Distribution):
         n (int) number of trials
     """
 
-    def __init__(self, prob=.5, size=20):
+    def __init__(self, prob=0.5, size=20):
 
         self.n = size
         self.p = prob
 
-        Distribution.__init__(self, self.calculate_mean(),
-                              self.calculate_stdev())
+        Distribution.__init__(self, self.calculate_mean(), self.calculate_stdev())
 
     def calculate_mean(self, round_to=2):
         """Function to calculate the mean from p and n
@@ -71,19 +71,18 @@ def replace_stats_with_data(self):
 
     def plot_bar(self):
         """Function to output a histogram of the instance variable data using
-        matplotlib pyplot library.
+        seaborn library.
 
         Args:
             None
 
         Returns:
             None
         """
-
-        plt.bar(x=['0', '1'], height=[(1 - self.p) * self.n, self.p * self.n])
-        plt.title('Bar Chart of Data')
-        plt.xlabel('outcome')
-        plt.ylabel('count')
+        sns.barplot(x=[0, 1], y=[(1 - self.p) * self.n, self.p * self.n]).set(
+            title="Bar Chart of Data", xlabel="outcome", ylabel="count"
+        )
+        plt.show()
 
     def calculate_pdf(self, k, round_to=2):
         """Probability density function calculator for the binomial distribution.
@@ -115,7 +114,7 @@ def calculate_cdf(self, k, round_to=2):
         """
 
         total_p = 0
-        for i in range(k+1):
+        for i in range(k + 1):
             self.calculate_pdf(i)
             total_p += self.pdf
         self.cdf = total_p
@@ -142,10 +141,10 @@ def plot_bar_pdf(self):
             y.append(self.pdf)
 
         # make the plots
-        plt.bar(x, y)
-        plt.title('Distribution of Outcomes')
-        plt.ylabel('Probability')
-        plt.xlabel('Outcome')
+
+        sns.barplot(x=x, y=y).set(
+            title="Distribution of Outcomes", ylabel="Probability", xlabel="Outcome"
+        )
 
         plt.show()
 
@@ -162,7 +161,7 @@ def __add__(self, other):
         """
 
         try:
-            assert self.p == other.p, 'p values are not equal'
+            assert self.p == other.p, "p values are not equal"
         except AssertionError as error:
             raise
 
@@ -184,5 +183,5 @@ def __repr__(self):
             string: characteristics of the Binomial
         """
 
-        return f'mean {self.mean}, standard deviation {self.stdev}, \
-            p {self.p}, n {self.n}'
+        return f"mean {self.mean}, standard deviation {self.stdev}, \
+            p {self.p}, n {self.n}"