All functions work well!

Central Limit Theorem.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[22]:
+
+
+import numpy
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# number of sample
+num = [1, 10, 50, 100] 
+# list of sample means
+means = [] 
+
+# Generating 1, 10, 30, 100 random numbers from -40 to 40
+# taking their mean and appending it to list means.
+for j in num:
+    # Generating seed so that we can get same result
+    # every time the loop is run...
+    numpy.random.seed(1)
+    x = [numpy.mean(
+        numpy.random.binomial(
+            1, 0.5, j)) for _i in range(1000)]
+    means.append(x)
+
+k = 0
+print(numpy.shape(means))
+# plotting all the means in one figure
+fig, ax = plt.subplots(2, 2, figsize =(8, 8))
+for i in range(0, 2):
+    for j in range(0, 2):
+        # Histogram for each x stored in means
+
+        ax[i, j].hist(means[k], 100, density = True)
+        ax[i, j].set_title(label = num[k])
+        k = k + 1
+
+plt.show()
+
+#what about binomial distribution?
+#[numpy.mean(
+        #numpy.random.binomial(
+        #    1, 0.5, j)) for _i in range(1000)]

Summary Statistic and Visualisation.py

@@ -0,0 +1,210 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import math
+import statistics
+import numpy as np
+import scipy.stats
+import pandas as pd
+
+
+# In[3]:
+
+
+x = [8.0, 1, 2.5, 4, 28.0]
+x_with_nan = [8.0, 1, 2.5, math.nan, 4, 28.0]
+
+print(x)
+print(x_with_nan)
+
+
+# In[5]:
+
+
+math.isnan(np.nan),np.isnan(math.nan)
+
+
+# In[15]:
+
+
+y, y_with_nan = np.array(x), np.array(x_with_nan)
+z, z_with_nan = pd.Series(x), pd.Series(x_with_nan)
+print("-------y------")
+print(y)
+print("-------y_with_nan------")
+print(y_with_nan)
+print("-------z------")
+print(z)
+print("-------z_with_nan------")
+print(z_with_nan)
+
+
+# In[16]:
+
+
+mean_ = sum(x) / len(x)
+mean_
+
+
+# In[6]:
+
+
+# Descriptive Statistics
+
+
+# In[7]:
+
+
+#Create a Dictionary of series
+d = {'Name':pd.Series(['Ahmet','Mehmet','Ayse','Berk','Mahmut','Aylin','Kazim',
+   'Lee','Temel','Gasper','Fatma','Elif']),
+   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
+   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
+}
+
+#Create a DataFrame
+df = pd.DataFrame(d)
+print(df)
+
+
+# In[8]:
+
+
+#Create a DataFrame
+df = pd.DataFrame(d)
+#summation
+print(df.sum())
+
+
+# In[11]:
+
+
+#select a column
+df['Age'].sum() 
+
+
+# In[12]:
+
+
+#apply a desired funtion on specified column(s)
+print("----VAR------")
+print(df[['Age','Rating']].var())
+
+print("----STD----")
+print(df[['Age','Rating']].std())
+
+
+# In[ ]:
+
+
+#Sr.No.	Function	Description
+#1	count()	Number of non-null observations
+#2	sum()	Sum of values
+#3	mean()	Mean of Values
+#4	median()	Median of Values
+#5	mode()	Mode of values
+#6	std()	Standard Deviation of the Values
+#7	var()	Variance of the Values
+#8	min()	Minimum Value
+#9	max()	Maximum Value
+#10	abs()	Absolute Value
+#11	prod()	Product of Values
+#12	cumsum()	Cumulative Sum
+#13	cumprod()	Cumulative Product
+
+
+# In[13]:
+
+
+print(df.describe())
+
+
+# In[54]:
+
+
+#Python Pandas - Visualization
+
+
+# In[26]:
+
+
+#np.random.seed(123)
+df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',
+   periods=10), columns=list('ABCD'))
+x
+df
+
+
+# In[27]:
+
+
+df.describe()
+#notice on the mean and std
+
+
+# In[28]:
+
+
+df.plot()
+
+
+# In[30]:
+
+
+#only non zero values
+nanzero_df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
+nanzero_df.plot.bar()
+
+
+# In[90]:
+
+
+nanzero_df.plot.bar(stacked=True)
+
+
+# In[92]:
+
+
+#horizontal
+nanzero_df.plot.barh(stacked=True)
+
+
+# In[33]:
+
+
+df = pd.DataFrame({'a':np.random.randn(1000),'b':np.random.randn(1000),'c':
+np.random.randn(1000)}, columns=['a', 'b', 'c'])
+
+#df.plot.hist(bins=20)
+df.describe()
+df['a'].plot.hist(bins=20)
+
+
+# In[34]:
+
+
+df.plot.box()
+
+
+# In[113]:
+
+
+df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
+df.plot.area()
+
+
+# In[114]:
+
+
+df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
+df.plot.scatter(x='a', y='b')
+
+
+# In[116]:
+
+
+df = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])
+df.plot.pie(subplots=True)

env/Lib/site-packages/PIL/BdfFontFile.py

@@ -0,0 +1,111 @@
+#
+# The Python Imaging Library
+# $Id$
+#
+# bitmap distribution font (bdf) file parser
+#
+# history:
+# 1996-05-16 fl   created (as bdf2pil)
+# 1997-08-25 fl   converted to FontFile driver
+# 2001-05-25 fl   removed bogus __init__ call
+# 2002-11-20 fl   robustification (from Kevin Cazabon, Dmitry Vasiliev)
+# 2003-04-22 fl   more robustification (from Graham Dumpleton)
+#
+# Copyright (c) 1997-2003 by Secret Labs AB.
+# Copyright (c) 1997-2003 by Fredrik Lundh.
+#
+# See the README file for information on usage and redistribution.
+#
+
+"""
+Parse X Bitmap Distribution Format (BDF)
+"""
+
+
+from . import FontFile, Image
+
+bdf_slant = {
+    "R": "Roman",
+    "I": "Italic",
+    "O": "Oblique",
+    "RI": "Reverse Italic",
+    "RO": "Reverse Oblique",
+    "OT": "Other",
+}
+
+bdf_spacing = {"P": "Proportional", "M": "Monospaced", "C": "Cell"}
+
+
+def bdf_char(f):
+    # skip to STARTCHAR
+    while True:
+        s = f.readline()
+        if not s:
+            return None
+        if s[:9] == b"STARTCHAR":
+            break
+    id = s[9:].strip().decode("ascii")
+
+    # load symbol properties
+    props = {}
+    while True:
+        s = f.readline()
+        if not s or s[:6] == b"BITMAP":
+            break
+        i = s.find(b" ")
+        props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
+
+    # load bitmap
+    bitmap = []
+    while True:
+        s = f.readline()
+        if not s or s[:7] == b"ENDCHAR":
+            break
+        bitmap.append(s[:-1])
+    bitmap = b"".join(bitmap)
+
+    [x, y, l, d] = [int(p) for p in props["BBX"].split()]
+    [dx, dy] = [int(p) for p in props["DWIDTH"].split()]
+
+    bbox = (dx, dy), (l, -d - y, x + l, -d), (0, 0, x, y)
+
+    try:
+        im = Image.frombytes("1", (x, y), bitmap, "hex", "1")
+    except ValueError:
+        # deal with zero-width characters
+        im = Image.new("1", (x, y))
+
+    return id, int(props["ENCODING"]), bbox, im
+
+
+class BdfFontFile(FontFile.FontFile):
+    """Font file plugin for the X11 BDF format."""
+
+    def __init__(self, fp):
+        super().__init__()
+
+        s = fp.readline()
+        if s[:13] != b"STARTFONT 2.1":
+            msg = "not a valid BDF file"
+            raise SyntaxError(msg)
+
+        props = {}
+        comments = []
+
+        while True:
+            s = fp.readline()
+            if not s or s[:13] == b"ENDPROPERTIES":
+                break
+            i = s.find(b" ")
+            props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
+            if s[:i] in [b"COMMENT", b"COPYRIGHT"]:
+                if s.find(b"LogicalFontDescription") < 0:
+                    comments.append(s[i + 1 : -1].decode("ascii"))
+
+        while True:
+            c = bdf_char(fp)
+            if not c:
+                break
+            id, ch, (xy, dst, src), im = c
+            if 0 <= ch < len(self.glyph):
+                self.glyph[ch] = xy, dst, src, im