added pands first tutorial

Author: learnp

.gitignore

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+*.pyc
+.idea/
+**/.idea/
+.ipynb_checkpoints/
+**/.ipynb_checkpoints/
+**/.cache/

.idea/misc.xml

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+import time
+def time_it(func):
+    def wrapper(*args, **kwargs):
+        start = time.time()
+        result = func(*args,**kwargs)
+        end = time.time()
+        print(func.__name__ +" took " + str((end-start)*1000) + "mil sec")
+        return result
+    return wrapper
+
+@time_it
+def calc_square(numbers):
+    result = []
+    for number in numbers:
+        result.append(number*number)
+    return result
+
+@time_it
+def calc_cube(numbers):
+    result = []
+    for number in numbers:
+        result.append(number*number*number)
+    return result
+
+array = range(1,100000)
+out_square = calc_square(array)
+out_cube = calc_cube(array)

.idea/py.iml

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+sum: 14 | 6,8
+sum: 13 | 7,6
+sum: 10 | 2,8
+sum: 14 | 9,5
+sum: 15 | 9,6

Advanced/decorators.py

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+s = "I work in bloomberg founded by bloomberg work work"
+
+tokens = s.split(" ")
+d = {}
+for token in tokens:
+    if token in d:
+        d[token] += 1
+    else:
+        d[token] = 1
+
+print(d)

Basics/input.txt

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+import pandas as pd
+
+stats = {
+    'Month': ['Jan', 'Feb', 'March', 'April'],
+    'Expenses': [2350, 3400, 2700, 2200],
+    'Income': [4000, 4000, 4000, 4000]
+}
+
+df = pd.DataFrame(stats)
+# df = df.set_index('Month')
+print(df.Month)

Basics/test.ipynb

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+# Pandas Tutorial On Stock Price Analysis
+
+# This tutorial will cover how to retrieve stock price from google finance using pandas data reader. The analysis of stock is done by plotting its high, low, close, volumne values in table and a chart. Charts are of two types,
+# 
+# 1. Line Chart
+# 2. Bar Chart
+# 
+# If you don't know what is stock then first **watch this video to gain understanding on fundamentals of stocks and investing**,
+# https://www.youtube.com/embed/XRO6lEu9-5w
+
+import pandas.io.data as web
+df = web.DataReader('AAPL', 'google', '2016/1/1', '2017/1/1')
+df.head()
+df.plot(y='Close', color="Green")
+df.plot.bar(y='Volume')
+

Basics/word_occurences.py

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+import matplotlib.pyplot as plt
+x = [1,2,3]
+y = [50,55,40]
+plt.plot(x,y)
+plt.xlabel('Day')
+plt.ylabel('Temperature')
+plt.title('Weather Chart')
+plt.show()

Modules/pandas_tutorial.py

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+import numpy as np
+
+a=np.array([[1,2,3],[4,5,6]])

jupyter/jupyter_architecture.png

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+import numpy as np
+import time
+import sys
+SIZE = 1000000
+l1 = range(SIZE)
+l2 = range(SIZE)
+a1=np.arange(SIZE)
+a2=np.arange(SIZE)
+
+# python list
+start = time.time()
+result = [(x+y) for x,y in zip(l1,l2)]
+print("python list took: ",(time.time()-start)*1000)
+# numpy array
+start= time.time()
+result = a1 + a2
+print("numpy took: ", (time.time()-start)*1000)
+
+