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Update for Python 3.x
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@romanbsd
romanbsd committed Sep 29, 2017
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64 changes: 32 additions & 32 deletions Examples/Basic/numpy-tutorial.py
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Expand Up @@ -35,33 +35,33 @@
#
#
## Lets get started!
print "Importing numpy"
print("Importing numpy")
import numpy as np

## This loads the numpy library and lets us refer to it by the shorthand "np",
## which is the convention used in the numpy documentation and in many
## online tutorials/examples

print "Creating arrays"
print("Creating arrays")
## Now lets make an array to play around with. You can make numpy arrays in
## a number of ways,
## Filled with zeros:
zeroArray = np.zeros( (2,3) ) # [[ 0. 0. 0.]
print zeroArray # [ 0. 0. 0.]]
print(zeroArray) # [ 0. 0. 0.]]

## Or ones:
oneArray = np.ones( (2,3) ) # [[ 1. 1. 1.]
print oneArray # [ 1. 1. 1.]]
print(oneArray) # [ 1. 1. 1.]]

## Or filled with junk:
emptyArray = np.empty( (2,3) )
print emptyArray
print(emptyArray)

## Note, emptyArray might look random, but it's just uninitialized which means
## you shouldn't count on it having any particular data in it, even random
## data! If you do want random data you can use random():
randomArray = np.random.random( (2,3) )
print randomArray
print(randomArray)

## If you're following along and trying these commands out, you should have
## noticed that making randomArray took a lot longer than emptyArray. That's
Expand All @@ -74,29 +74,29 @@
[4,5,6]]

myArray = np.array(foo) # [[1 2 3]
print myArray # [4 5 6]]
print(myArray) # [4 5 6]]


print "Reshaping arrays"
print("Reshaping arrays")
## Of course, if you're typing out a range for a larger matrix, it's easier to
## use arange(...):
rangeArray = np.arange(6,12).reshape( (2,3) ) # [[ 6 7 8]
print rangeArray # [ 9 10 11]]
print(rangeArray) # [ 9 10 11]]

## there's two things going on here. First, the arange(...) function returns a
## 1D array similar to what you'd get from using the built-in python function
## range(...) with the same arguments, except it returns a numpy array
## instead of a list.
print np.arange(6,12) # [ 6 7 8 9 10 11 12]
print(np.arange(6,12)) # [ 6 7 8 9 10 11 12]

## the reshape method takes the data in an existing array, and stuffs it into
## an array with the given shape and returns it.
print rangeArray.reshape( (3,2) ) # [[ 6 7]
print(rangeArray.reshape( (3,2) )) # [[ 6 7]
# [ 8 9]
# [10 11]]

#The original array doesn't change though.
print rangeArray # [[ 6 7 8]
print(rangeArray) # [[ 6 7 8]
# [ 9 10 11]

## When you use reshape(...) the total number of things in the array must stay
Expand All @@ -106,69 +106,69 @@
squareArray = np.arange(1,10).reshape( (3,3) ) #this is fine, 9 elements


print "Accessing array elements"
print("Accessing array elements")
## Accessing an array is also pretty straight forward. You access a specific
## spot in the table by referring to its row and column inside square braces
## after the array:
print rangeArray[0,1] #7
print(rangeArray[0,1]) #7

## Note that row and column numbers start from 0, not 1! Numpy also lets you
## refer to ranges inside an array:
print rangeArray[0,0:2] #[6 7]
print squareArray[0:2,0:2] #[[1 2] # the top left corner of squareArray
print(rangeArray[0,0:2]) #[6 7]
print(squareArray[0:2,0:2]) #[[1 2] # the top left corner of squareArray
# [4 5]]

## These ranges work just like slices and python lists. n:m:t specifies a range
## that starts at n, and stops before m, in steps of size t. If any of these
## are left off, they're assumed to be the start, the end+1, and 1 respectively
print squareArray[:,0:3:2] #[[1 3] #skip the middle column
print(squareArray[:,0:3:2]) #[[1 3] #skip the middle column
# [4 6]
# [7 9]]

## Also like python lists, you can assign values to specific positions, or
## ranges of values to slices
squareArray[0,:] = np.array(range(1,4)) #set the first row to 1,2,3
squareArray[0,:] = np.array(list(range(1,4))) #set the first row to 1,2,3
squareArray[1,1] = 0 # set the middle spot to zero
squareArray[2,:] = 1 # set the last row to ones
print squareArray # [[1 2 3]
print(squareArray) # [[1 2 3]
# [4 0 6]
# [1 1 1]]

## Something new to numpy arrays is indexing using an array of indices:
fibIndices = np.array( [1, 1, 2, 3] )
randomRow = np.random.random( (10,1) ) # an array of 10 random numbers
print randomRow
print randomRow[fibIndices] # the first, first, second and third element of
print(randomRow)
print(randomRow[fibIndices]) # the first, first, second and third element of
# randomRow

## You can also use an array of true/false values to index:
boolIndices = np.array( [[ True, False, True],
[False, True, False],
[ True, False, True]] )
print squareArray[boolIndices] # a 1D array with the selected values
print(squareArray[boolIndices]) # a 1D array with the selected values
# [1 3 0 1 1]

## It gets a little more complicated with 2D (and higher) arrays. You need
## two index arrays for a 2D array:
rows = np.array( [[0,0],[2,2]] ) #get the corners of our square array
cols = np.array( [[0,2],[0,2]] )
print squareArray[rows,cols] #[[1 3]
print(squareArray[rows,cols]) #[[1 3]
# [1 1]]
boolRows = np.array( [False, True, False] ) # just the middle row
boolCols = np.array( [True, False, True] ) # Not the middle column
print squareArray[boolRows,boolCols] # [4 6]
print(squareArray[boolRows,boolCols]) # [4 6]

print "Operations on arrays"
print("Operations on arrays")
## One useful trick is to create a boolean matrix based on some test and use
## that as an index in order to get the elements of a matrix that pass the
## test:
sqAverage = np.average(squareArray) # average(...) returns the average of all
# the elements in the given array
betterThanAverage = squareArray > sqAverage
print betterThanAverage #[[False False True]
print(betterThanAverage) #[[False False True]
# [ True False True]
# [False False False]]
print squareArray[betterThanAverage] #[3 4 6]
print(squareArray[betterThanAverage]) #[3 4 6]

## Indexing like this can also be used to assign values to elements of the
## array. This is particularly useful if you want to filter an array, say by
Expand All @@ -188,32 +188,32 @@
# truncate them down to integers.
clampedSqArray[ (squareArray-sqAverage) > sqStdDev ] = sqAverage+sqStdDev
clampedSqArray[ (squareArray-sqAverage) < -sqStdDev ] = sqAverage-sqStdDev
print clampedSqArray # [[ 1. 2. 3. ]
print(clampedSqArray) # [[ 1. 2. 3. ]
# [ 3.90272394 0.31949828 3.90272394]
# [ 1. 1. 1. ]]


## Multiplying and dividing arrays by numbers does what you'd expect. It
## multiples/divides element-wise
print squareArray * 2 # [[ 2 4 6]
print(squareArray * 2) # [[ 2 4 6]
# [ 8 0 12]
# [ 2 2 2]]

## Addition works similarly:
print squareArray + np.ones( (3,3) ) #[[2 3 4]
print(squareArray + np.ones( (3,3) )) #[[2 3 4]
# [5 1 7]
# [2 2 2]]

## Multiplying two arrays together (of the same size) is also element wise
print squareArray * np.arange(1,10).reshape( (3,3) ) #[[ 1 4 9]
print(squareArray * np.arange(1,10).reshape( (3,3) )) #[[ 1 4 9]
# [16 0 36]
# [ 7 8 9]]

## Unless you use the dot(...) function, which does matrix multiplication
## from linear algebra:
matA = np.array( [[1,2],[3,4]] )
matB = np.array( [[5,6],[7,8]] )
print np.dot(matA,matB) #[[19 22]
print(np.dot(matA,matB)) #[[19 22]
# [43 50]]

## And thats it! There's a lot more to the numpy library, and there are a few
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74 changes: 37 additions & 37 deletions Examples/Basic/pandas-tutorial.py
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Expand Up @@ -20,67 +20,67 @@
for i in range(1, 6):
ldt_timestamps.append(dt.datetime(2011, 1, i, 16))

print "The index we created has the following dates : "
print ldt_timestamps
print
print("The index we created has the following dates : ")
print(ldt_timestamps)
print()

## TimeSeries
ts_single_value = pd.TimeSeries(0.0, index=ldt_timestamps)
print "A timeseries initialized to one single value : "
print("A timeseries initialized to one single value : ")

na_vals = np.arange(len(ldt_timestamps))
print "Dummy initialized array : "
print na_vals
print
print("Dummy initialized array : ")
print(na_vals)
print()

ts_array = pd.TimeSeries(na_vals, index=ldt_timestamps)
print "A timeseries initialized using a numpy array : "
print ts_array
print
print("A timeseries initialized using a numpy array : ")
print(ts_array)
print()

print "Reading the timeseries for a particular date"
print "Date : ", ldt_timestamps[1]
print "Value : ", ts_array[ldt_timestamps[1]]
print
print("Reading the timeseries for a particular date")
print("Date : ", ldt_timestamps[1])
print("Value : ", ts_array[ldt_timestamps[1]])
print()

print "Initializing a list of symbols : "
print("Initializing a list of symbols : ")
ls_symbols = ['AAPL', 'GOOG', 'MSFT', 'IBM']
print ls_symbols
print
print(ls_symbols)
print()

print "Initializing a dataframe with one value : "
print("Initializing a dataframe with one value : ")
df_single = pd.DataFrame(index=ldt_timestamps, columns=ls_symbols)
df_single = df_single.fillna(0.0)
print df_single
print
print(df_single)
print()

print "Initializing a dataframe with a numpy array : "
print("Initializing a dataframe with a numpy array : ")
na_vals_2 = np.random.randn(len(ldt_timestamps), len(ls_symbols))
df_vals = pd.DataFrame(na_vals_2, index=ldt_timestamps, columns=ls_symbols)
print df_vals
print
print(df_vals)
print()

print "Access the timeseries of a particular symbol : "
print df_vals[ls_symbols[1]]
print
print("Access the timeseries of a particular symbol : ")
print(df_vals[ls_symbols[1]])
print()

print "Access the timeseries of a particular date : "
print df_vals.ix[ldt_timestamps[1]]
print
print("Access the timeseries of a particular date : ")
print(df_vals.ix[ldt_timestamps[1]])
print()

print "Access the value for a specific symbol on a specific date: "
print df_vals[ls_symbols[1]].ix[ldt_timestamps[1]]
print
print("Access the value for a specific symbol on a specific date: ")
print(df_vals[ls_symbols[1]].ix[ldt_timestamps[1]])
print()

print "Reindexing the dataframe"
print("Reindexing the dataframe")
ldt_new_dates = [dt.datetime(2011, 1, 3, 16),
dt.datetime(2011, 1, 5, 16),
dt.datetime(2011, 1, 7, 16)]
ls_new_symbols = ['AAPL', 'IBM', 'XOM']
df_new = df_vals.reindex(index=ldt_new_dates, columns=ls_new_symbols)
print df_new
print "Observe that reindex carried over whatever values it could find and set the rest to NAN"
print
print(df_new)
print("Observe that reindex carried over whatever values it could find and set the rest to NAN")
print()

print "For pandas rolling statistics please refer : http://pandas.pydata.org/pandas-docs/dev/computation.html#moving-rolling-statistics-moments"
print("For pandas rolling statistics please refer : http://pandas.pydata.org/pandas-docs/dev/computation.html#moving-rolling-statistics-moments")

4 changes: 2 additions & 2 deletions Examples/Basic/tutorial1.py
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Expand Up @@ -21,7 +21,7 @@
import matplotlib.pyplot as plt
import pandas as pd

print "Pandas Version", pd.__version__
print("Pandas Version", pd.__version__)


def main():
Expand Down Expand Up @@ -49,7 +49,7 @@ def main():
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
d_data = dict(list(zip(ls_keys, ldf_data)))

# Filling the data for NAN
for s_key in ls_keys:
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10 changes: 5 additions & 5 deletions Examples/Basic/tutorial2.py
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Expand Up @@ -33,11 +33,11 @@ def main():
ls_symbols = ['$SPX', 'XOM', 'GOOG', 'GLD']

# Printing the first 5 rows
print "First 5 rows of Price Data:"
print na_price[:5, :]
print
print "First 5 rows of Dates:"
print na_dates[:5, :]
print("First 5 rows of Price Data:")
print(na_price[:5, :])
print()
print("First 5 rows of Dates:")
print(na_dates[:5, :])

# Creating the timestamps from dates read
ldt_timestamps = []
Expand Down
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