Section 1
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What is the output of the following code: Code: import numpy as np import pandas as pd s = pd.DataFrame([0, 1, 2, np.nan]) print s.size, s.count() Select one answer: a) 3 0 3 b) 3 0 4 c) 4 0 3 d) 4 0 4
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Cards (195)
Section 1
(50 cards)
What is the output of the following code: Code: import numpy as np import pandas as pd s = pd.DataFrame([0, 1, 2, np.nan]) print s.size, s.count() Select one answer: a) 3 0 3 b) 3 0 4 c) 4 0 3 d) 4 0 4
c
How should section A be filled in to complete code that will cause the following output: Code: import pandas as pd df = pd.DataFrame({ "A": [1, 2], "B": [3, 4] }) print df print __A__ print df Output: A B 0 1 3 1 2 4 A 0 1 1 2 A B 0 1 3 1 2 4 Select one answer: a) df.drop("B", inplace=True) b) df.drop("B", axis=0) c) df.drop("B", axis=1) d) df.drop("B", axis=1, inplace=True)
c
What statement in place of _A_ would cause the following output: Code: import numpy as np x = np.random.randint(5, size=(4, 5)) print x _A_ print x Output: [[4 2 3 4 1] [2 2 1 0 1] [2 1 0 3 4] [3 2 1 1 3]] [[ 4 2 3 4 1] [777 2 777 0 777] [ 2 1 0 3 4] [ 3 2 1 1 3]] Select one answer: a) x[1][2::] = 777 b) x[1:3:5] = 777 c) x[1::3] = 777 d) x[1][::2] = 777
d
What is the output of the variable "value"? import numpy as np a = np.random.randint(0,10,size=(3,3)) print "Matrix a is ",a value = np.mean(a.min(axis = 0)) print "Variable value is", value OUTPUT ====== Matrix a is [[7 5 2] [8 5 1] [1 3 0]] Select one answer: a) 1.0 b) [5.33333333 4.33333333 1.0] c) 1.3333333 d) [4.66666667 4.66666667 1.33333333]
c
How should section A be filled so that the code causes the following output? Code: import numpy as np j=np.random.random([4,4]) print j print A Output: [[ 0.77193745 0.65987068 0.07110931 0.34828411] [ 0.57139421 0.58080777 0.45935194 0.05061515] [ 0.01467635 0.84673314 0.78251514 0.96852681] [ 0.71871822 0.57120611 0.30561734 0.71769405]] [[ 0.45935194 0.05061515] [ 0.78251514 0.96852681]] Select one answer: a)j[1:2,2:] b)j[2:3,3:4] c)j[1:3,2:] d)j[2:,1:2]
c
What is the output of the following code? Given a CSV file that contains the following data: Name,Value,Company,Founded George Soros,4000000000,Soros Fund Management,1969 Tucker Balch,1000000000000,Lucena Research, import pandas as pd df = pd.read_csv('top_hedgefund_managers.csv', index_col="Company").sort(columns='Value', ascending=False).fillna('TBD') print(df) Select one answer: a) Name Value Founded Company Lucena Research Tucker Balch 1000000000000 TBD Soros Fund Management George Soros 4000000000 1969 b) Name Value Founded Company Soros Fund Management George Soros 4000000000 1969 Lucena Research Tucker Balch 1000000000000 TBD c) Name Value Founded Company Lucena Research Tucker Balch 1000000000000 NaN Soros Fund Management George Soros 4000000000 1969 d) Value Company Founded Name Tucker Balch 1000000000000 Lucena Research TBD George Soros 4000000000 Soros Fund Management 1969
a
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np x = np.array([[0, 1, 2, 3, 4],\ [5, 6, 7, 8, 9],\ [10, 11, 12, 13, 14]]) print x print _A_ Output: [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]] [[3] [8]] Select one answer: a) x[:2,4] b) x[3:4,0:2] c) x[2,3] d) x[0:2,3:4]
d
What is the output of the following python code: Code: import numpy as np import pandas as pd df = pd.DataFrame(np.array([(1,2),(3,4),(5,6)]),index=['a','c','e'],columns=['data1','data2']) df = df.reindex(['a','b','c','d','e']) df = df.fillna(method='ffill') df = df.sum(axis=1) print df Select one answer: a) data1 13 data2 18 dtype: float64 b) a 3 b 3 c 7 d 7 e 11 dtype: float64 c) data1 17 data2 22 dtype: float64 d) a 3 b 7 c 7 d 11 e 11 dtype: float64
b
What is the output of this Python code? Code: import numpy as np j = np.array([(1,2,3),(4,5,6),(7,8,9)]) k = j[:,-1:] print k.shape Select one answer: a) 3 b) (3, 1) c) (1, 3) d) [3 6 9]
b
How would section _A_ be filled to complete code that will address any gaps in the data (missing cells) after reindexing a data frame and produce the following output: Code: import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(5, 3), index=['2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-08'], columns=['a', 'b', 'c']) df = df.reindex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05','2013-01-07', '2013-01-08', '2013-01-09']) print df _A_ print df Output: a b c 2013-01-01 NaN NaN NaN 2013-01-02 0.490073 0.132083 -0.404633 2013-01-03 0.972129 0.596112 -0.744198 2013-01-04 -0.650304 0.299980 0.093164 2013-01-05 -1.049114 -0.212860 0.698289 2013-01-07 NaN NaN NaN 2013-01-08 0.671206 -0.611449 -0.215637 2013-01-09 NaN NaN NaN a b c 2013-01-01 0.490073 0.132083 -0.404633 2013-01-02 0.490073 0.132083 -0.404633 2013-01-03 0.972129 0.596112 -0.744198 2013-01-04 -0.650304 0.299980 0.093164 2013-01-05 -1.049114 -0.212860 0.698289 2013-01-07 -1.049114 -0.212860 0.698289 2013-01-08 0.671206 -0.611449 -0.215637 2013-01-09 0.671206 -0.611449 -0.215637 Select one answer: a) df.fillna(method='ffill', inplace=True) b) df.fillna(method='bfill', inplace=True) c) a followed by b d) b followed by a
c
What is the output of the following python codes? Codes: import numpy as np a = np.array([1,2,3,4,5]) print a * a[::-1] Select one answer: a) [5 8 9 8 8] b) [5 8 8 8 5] c) [5 8 9 8 5] d) [5 9 9 8 5]
c
What is the output of the following code snippet? Code: import numpy as np m = np.array([[1, 3, 5], [2, 4, 6]]) n = np.array([[1, 2, 3], [1, 2, 3]]) print m.sum(axis=0)+n.mean(axis=0) Select one answer: a) [ 5, 11, 17] b) 33 c) [ 11., 14.] d) [ 4., 9., 14.]
d
Fill the blank __A__ below to cause this Python code to give the following output: Code: import pandas as pd i= [1,2,3,4,5,6] d = [2,2.5,2.99,3.5,3.8,10] df = pd.DataFrame(index=i, data=d) print __A__ Output: 0 1 NaN 2 0.250000 3 0.196000 4 0.170569 5 0.085714 6 1.631579 Select one answer: a) df/df.shift(1) - 1 b) df/(df-1) c) df/df.shift(1) d) df*(df-1)
a
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np m = np.arange(20).reshape(4,5) print m print m.__A__ Output: [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19]] [[ 0 1 3 6 10] [ 5 11 18 26 35] [10 21 33 46 60] [15 31 48 66 85]] Select one answer: a) m.sum(axis=0) b) m.sum(axis=1) c) m.cumsum(axis=0) d) m.cumsum(axis=1)
d
What is the output of this Python code? Code: import pandas as pd data = {'A': [100, 60, 30, 130], 'B': [5, 15, 15, 5], 'C': [100, 300, 100, 200], 'D': [20, 20, 30, 30]} df = pd.DataFrame(data) # print df # A B C D #0 100 5 100 20 #1 60 15 300 20 #2 30 15 100 30 #3 130 5 200 30 df = df.shift(2) print df.mean() Select one answer: a) A 40 B 5 C 100 D 10 b) A 80 B 10 C 200 D 20 c) A 80 B 10 C 175 D 25 d) A NaN B NaN C NaN D NaN
b
What is the output of this python code? Code: import numpy as np arr = np.array([[1,2,5],[2,4,2],[3,3,2]]) x = arr[:,1].sum(),arr[0:].sum().sum(axis=0) print x Select one answer: a) (9,24) b) (9,8) c) (8,24) d) 33
a
red = np.array([[3,4],[2,4]]) x = ([2,5],[2,3]) x = np.asarray(x) blue = red * x[-2,:] print blue Select one answer: a)[[3 4] [2 4]] b)[[ 6 20] [ 4 20]] c)[[2 5] [2 3]] d)[[6 8] [4 8]]
b
What is the output of the following code? Code: import numpy as np a = np.array([[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15], [16,17,18,19,20], [21,22,23,24,25]]) print a[:,1:5:2] Select one answer: a) [[ 1 3 5] [ 6 8 10] [11 13 15] [16 18 20] [21 23 25]] b) [[ 2 4] [ 7 9] [12 14] [17 19] [22 24]] c) [[ 1 2 3 4 5] [11 12 13 14 15] [21 22 23 24 25]] c) [[ 6 7 8 9 10] [16 17 18 19 20]]
b
What is the output of the following python code? import numpy as np x = np.array([[0,1,2],[3,4,5],[4,3,2]]) x = x**2 print x[-1] * x[1] A)[144 144 100] B)[0 4 10] C)[0 16 100] D)[48 48 40]
a
How should section A be filled in to complete code that will cause the following output: (Select all the values bigger than 10 from the second column) Code: import numpy as np r = np.random.randint(20, size=(4, 6)) print r print _A_ Output: [[18 15 12 12 17 7] [18 6 9 16 5 2] [ 9 18 11 10 6 18] [12 8 19 19 4 16]] [[15 18]] Select one answer: a) r[np.where(r[:,0]>10),0] b) r[np.where(r[:,1]>10),1] c) r[1,np.where(r[:,1]>10)] d) r[0,np.where(r[:,0]>10)]
b
What is the output of the following code? Code: import pandas as pd import numpy as np syms=['IBM', 'AAPL', 'HNZ', 'XOM', 'GLD'] prices = pd.DataFrame(np.random.rand(10, len(syms)),columns=syms) print prices.tail(1).values Select one answer: a:) IBM AAPL HNZ XOM GLD 5 0.596553 0.654170 0.020114 0.889447 0.024598 6 0.557738 0.478691 0.011598 0.812025 0.668150 7 0.231585 0.993491 0.302910 0.261203 0.659507 8 0.715009 0.244946 0.644569 0.415497 0.827711 9 0.282840 0.814612 0.542779 0.325938 0.387805 b:) IBM AAPL HNZ XOM GLD 0 0.021792 0.41759 0.628591 0.834644 0.118658 c:) [[ 0.28284041 0.81461207 0.54277906 0.32593834 0.3878054 ]] d:) IBM AAPL HNZ XOM GLD 9 0.28284 0.814612 0.542779 0.325938 0.387805
c
What is the output of the following python code? Code: import numpy as np ary = np.array([[[1, 2], [3, 4]], [[5, 6], [7,8]]]) print ary[:, :, 0] Output of "print ary" is: [[[1 2] [3 4]] [[5 6] [7 8]]] Select one answer: a) [[1 2] [3 4]] b) [[1 2] [5 6]] c) [[1 3] [5 7]] d) [[1 5] [3 7]]
c
What is the output of the following python code? Code: import pandas as pd data = { 'key': [ 'B','D','C','A' ], 'value': [ 1,2,3,4 ]} sub = pd.DataFrame.from_dict( data ).sort( columns=[ 'key' ]).ix[ 0:1 ] print sub.value.mean() Select one answer: a) 1.5 b) 2.0 c) 2.5 d) 3.0
b
How should section _A_ be filled in to complete code that will cause the following output: Code: import numpy as np a = np.ones((3,3)) print a b = a b[0,1] = 2 print b print a b = _A_ # what should be filled instead of _A_ for below ouputs to be true ? b[0,1] = 3 print b print a Output: [[ 1. 1. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 2. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 2. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 3. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 2. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] Select one answer: a) a[0,0] = 2 b) a.copy() c) b[1,1] = 3 d) b[1,0] = 3
b
What is the output of this python code? Code: import pandas as pd df1 = pd.DataFrame({'Type': ["Stock", None]}) df2 = df1 df1.fillna("Mutual Fund", inplace=True) df2.fillna("ETF") print df1 print df2 Select one answer: a) Type 0 Stock 1 None Type 0 Stock 1 ETF b) Type 0 Stock 1 Mutual Fund Type 0 Stock 1 ETF c) Type 0 Stock 1 Mutual Fund Type 0 Stock 1 None d) Type 0 Stock 1 Mutual Fund Type 0 Stock 1 Mutual Fund
d
1. What is the output of the following code Code: j = [8, 7, 6, 5, 4, 3, 2, 1] print [x/j[-2] for x in j[1:-1]] Select one answer: a) [4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.0, 0.5] b) [3.5, 3.0, 2.5, 2.0, 1.5, 1.0, 0.5] c) [3, 3, 2, 2, 1, 1] d) [3, 3, 2, 2, 1, 1, 0]
c
You are given two data frames, df1 and df2, in the code below. They are joined together to form a third data frame. What is the output of the below code? Code: import pandas as pd df1 = pd.DataFrame( {'var1': [1,2,3,4,5], 'var2': [11,12,13,14,15]}, index = ['cat','dog', 'bird', 'fish', 'turtle'] ) df2 = pd.DataFrame( {'var3': [101,102,103,104,105]}, index = ['dog', 'bird', 'cat', 'turtle', 'fish'] ) df3 = df1.join(df2) print df3.ix['bird','var2'] Select one answer: a) [11, 12, 13, 14, 15] b) [3, 13, 102] c) 13 d) 102
c
A donut shop owner sells 3 items (coffee, donuts, and bagels) and wants to calculate the following for a week of sales (1) total sales, (2) daily sales, and (3) item sales Code: import numpy as np import pandas as pd weekSales = pd.DataFrame(np.random.random([7,3])*100, index=['Sun', 'Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat'], columns=['Coffee', 'Donuts', 'Bagels']) print weekSales print _A_ print _B_ print _C_ Output: Coffee Donuts Bagels Sun 33.530433 9.862755 53.781308 Mon 29.831218 84.473341 34.325790 Tues 78.021005 84.196225 27.752726 Wed 85.667444 2.104389 0.696575 Thurs 74.328857 56.910230 24.484673 Fri 76.807376 67.794258 17.389399 Sat 84.438981 3.740902 58.141248 988.27913306218318 Sun 97.174496 Mon 148.630350 Tues 189.969956 Wed 88.468408 Thurs 155.723760 Fri 161.991033 Sat 146.321131 Coffee 462.625316 Donuts 309.082100 Bagels 216.571718 What code A, B, and C are used to produce that output? Select one answer: a) weekSales.sum(), weekSales.sum(axis=1), weekSales.sum(axis=0) b) weekSales.sum().sum(), weekSales.sum(axis=0), weekSales.sum(axis=1) c) neither a nor b d) either a or b
c
What is the output of the following code? import numpy as np a = np.array([2]*4) b = np.array([1, 2, 3, 4, 5, 6, 7]) b[1:] * a[-1] Select one answer: a) array([ 4, 6, 8, 10, 12, 14]) b) array([ 1, 2, 3, 4, 5, 6, 7]) c) array([ 2, 4, 6, 8, 10, 12, 14]) d) array([ 3, 9, 12, 15, 18])
a
How should section A be filled in to complete code that will cause the following output: Code: import pandas as pd import numpy as np x = True df1 = pd.DataFrame([[1,0,-5],[2,0,-1],[1,4,0]]) ___A___ df1[f(df1)] = "!!!" print df1 Output: 0 1 2 0 1 0 !!! 1 !!! 0 -1 2 1 !!! 0 Select one answer: a) f = lambda x: x>1 b) f = lambda x: np.abs(x)>=0 c) f = lambda x: np.abs(x)>1 d) f = lambda x: x>0
c
Which of the following is a valid output of the following code? Code: import numpy as np a = np.random.randint(10, 30, size=(2, 4)) print a print a.size print a.shape[0] print a.shape[1] Select one answer: a) [[5 14 10 10] [29 26 40 13]] 8 2 4 b) [[21 14 10 10] [29 26 19 13]] 8 4 2 c) [[21 14 10 10] [29 26 19 13]] 8 2 4 d) [[21 14] [0 26] [29 15] [12 4]] 4 2 4
c
"How should section A be filled in to complete code that will cause the following output:" # Code import numpy as np x = np.array([[1,2,3,4], [4,5,6,7], [8,9,0,1]]) print _A_ """ Output: [[1, 3], [4, 6], [8, 0]]) Select one answer: a) x[0,2] b) x[:,0,2] c) x[::2] d) x[:,[0,2]]
d
What is the output of the following code? import pandas as pd import numpy as np array0 = np.array([3,2,5,2,1]) df = pd.DataFrame(array0, columns=['numbers'], index=['a','b','c','d','e']) df = df.ix[df.index[2:5]].sum() dr = df**2 print df Select one answer: a) 64 b) numbers 8 c) 8 d) 9
b
What will be the output of the following code snippet, specifically the final print statement? Code: import numpy as np array = np.ones((2,3,4)) array = array * 2 print array.sum(axis=None) Select one answer: a) 24 b) [[ 6. 6. 6. 6.] [ 6. 6. 6. 6.]] c) [[ 8. 8. 8.] [ 8. 8. 8.]] d) 48
d
What is the output of the following code?
>>>import numpy as np
>>>a=np.random.randint(0,10,size=(5,4))
>>>print 'a=
',a
a=
[[4 1 6 9]
[4 9 2 5]
[7 0 9 7]
[6 1 9 5]
[6 6 3 9]]
what is the output of the following code?
print a[1:3,1:2].sum()
a) 25
b) 26
c) 11
d) 9
d
What line of code should be added to "Section A", using pandas DataFrames, so that it produces the following output: Code: import pandas as pd df1 = pd.DataFrame([[10, 'w'], [20, 'x'], [30, 'y'], [40, 'z']], columns=['numbers', 'letters'], index=['a', 'b', 'c', 'd']) df2 = pd.DataFrame([[1.0, '#'], [2.0, '@'], [3.0, '%'], [4.0, '$']], columns=['floats', 'symbols'], index=['a', 'b', 'c', 'e']) # Section A Output: numbers letters floats symbols a 10 w 1 # b 20 x 2 @ c 30 y 3 % d 40 z NaN NaN e NaN NaN 4 $ Select one answer: a) print df1.join(df2, how='right') b) print df1.join(df2, how='inner') c) print df1.join(df2, how='outer') d) print df1.join(df2, how='left')
c
Which answer is the output of the following code? import pandas as pd import numpy as np d = {"SPY" : [86.80, 86.70, 87.28, 84.67, 85.01], "AAPL": [90.36, 94.18, 92.62, 90.62, 92.30], "HNZ" : [33.95, 33.82, 33.38, 32.59, 31.99], "XOM" : [74.48, 74.47, 73.26, 71.39, 85.13], "GLD" : [86.23, 84.48, 85.13, 82.75, 84.46]} df = pd.DataFrame(d) normed = df/df.ix[0] normed['AAPL'] = np.nan normed.fillna(value='0') print normed[0:2] a) AAPL GLD HNZ SPY XOM 1 NaN 0.979705 0.996171 0.998848 0.999866 2 NaN 0.987243 0.983211 1.005530 0.983620 b) AAPL GLD HNZ SPY XOM 1 94.18 84.48 33.82 86.70 74.47 2 92.62 85.13 33.38 87.28 73.26 c) AAPL GLD HNZ SPY XOM 0 0 1.000000 1.000000 1.000000 1.000000 1 0 0.979705 0.996171 0.998848 0.999866 d) AAPL GLD HNZ SPY XOM 0 NaN 1.000000 1.000000 1.000000 1.000000 1 NaN 0.979705 0.996171 0.998848 0.999866
d
What is the output of the following code? import numpy as np a=np.ones((3,3))*2 b=a.dot(a) print(a) print(b) A) [[ 2. 2. 2.] [ 2. 2. 2.] [ 2. 2. 2.]] [[ 4. 4. 4.] [ 4. 4. 4.] [ 4. 4. 4.]] B) [[ 2. 2. 2.] [ 2. 2. 2.] [ 2. 2. 2.]] [[ 12. 12. 12.] [ 12. 12. 12.] [ 12. 12. 12.]] C) [[ 4. 4. 4.] [ 4. 4. 4.] [ 4. 4. 4.]] [[ 12. 12. 12.] [ 12. 12. 12.] [ 12. 12. 12.]] D) [[ 2. 2. 2.] [ 2. 2. 2.] [ 2. 2. 2.]] [[ 8. 8. 8.] [ 8. 8. 8.] [ 8. 8. 8.]]
b
What is the output of the following Python code? Code: import numpy as np import pandas as pd arr = np.ones([1,5]) df = pd.DataFrame(arr) df.ix[:,2:3] = 0 print df.values Select one answer: a) [1, 1, 0, 1, 1] b) [1, 1, 0, 0, 1] c) [1, 1, 1, 1, 1] d) [0, 0, 0, 0, 0]
b
What will be the output of the following code: a = [[]]*4 a[0].append(15) print a Select one answer: a) [[[15]], [[]], [[]], [[]]] b) [[15], [15], [15], [15]] c) [[15], [], [], []] d) [[[15]], [[15]], [[15]], [[15]]]
b
What is the output of the joined Dataframe? Code: import numpy as np import pandas as pd df = pd.DataFrame([52, 46, 50, 51], columns = ['AAPL'], index = ['01-01', '01-02', '01-03', '01-04']) df = df.join(pd.DataFrame([83, 88, 86, 90], columns = ['SPY'], index = ['01-01', '01-02', '01-04', '01-05'])) print df Select one answer: a) AAPL SPY 01-01 52 83 01-02 46 88 01-03 50 86 01-04 51 90 b) AAPL SPY 01-01 52 83 01-02 46 88 01-03 50 NaN 01-04 51 86 c) AAPL SPY 01-01 52 83 01-02 46 88 01-03 50 NaN 01-04 51 86 01-05 NaN 90 d) AAPL SPY 01-01 52 83 01-02 46 88 01-04 51 86
b
What is the output of the following code? Code: import numpy as np A = np.zeros((4,4), dtype=int) A[1::2, ::2] = 1 A[::2, 1::2] = 1 print(A) Select one answer: a) [[1 0 1 0] [0 1 0 1] [1 0 1 0] [0 1 0 1]] b) [[0 1 0 1] [1 0 1 0] [0 1 0 1] [1 0 1 0]] c) [[0 1 0 0] [1 1 0 0] [0 0 0 0] [0 0 0 0]] d) None of the above
b
How should section A be filled in to complete code that will cause the following output: import numpy as np import pandas as pd dates = pd.date_range('2015-01-01', '2015-01-04', name='Date') df = pd.DataFrame(index=dates) df['count'] = [i for i in range(len(df))] print df __A__ df.set_index('count', inplace=True) print df Output: count Date 2015-01-01 0 2015-01-02 1 2015-01-03 2 2015-01-04 3 Date count 0 2015-01-01 1 2015-01-02 2 2015-01-03 3 2015-01-04 Select one answer: a) df.reset_index(['Date']) b) df.drop_index(['Date']) c) df = df.reset_index(['Date']) d) df.drop_index(['Date'], inplace=True)
c
What is the output of this Python code? Code: import numpy as np j = np.array([(1,2,3),(4,5,6),(7,8,9)]) k = j[:,-1:] print k.shape Select one answer: a) 3 b) (3, 1) c) (1, 3) d) [3 6 9]
b
What is the output of the following code? import pandas import numpy df = numpy.array([2, 3, 4, 5]) print pandas.rolling_mean(df, window=2) Select one: a) [nan 2 3 4] b) [nan 2.5 3.5 4.5] c) [2 2.5 3.25 4.125] d) [1 2 3 4]
b
What is the output of the following code: Code: import pandas import numpy df = numpy.array([25,4,78,54,6,21,45,2]) print pandas.rolling_max(df,window=2,min_periods=2) Output: Select one answer: a)[ nan 78. 78. 78. 54. 21. 45. 45.] b)[ nan nan 25. 78. 78. 54. 21. 45.] c)[ nan 6. 25. 78. 54. 21. 45. 45.] d)[ nan 25. 78. 78. 54. 21. 45. 45.]
d
What is the output of this Python code? import numpy as np x = np.array([8,1,3,4,5,2,1,9,6,4,7]) print(np.mean(x[2:7][::-1][:-1])) Select one answer: a) 5 b) 3 c) 5.0 d) 3.0
d
What is the output of this code? Code: import numpy as np k= np.array([[2, 4, 8, 16], [16.0, 8.0, 4.0, 2.0]]) print k[1,2:-1]/k[-2,-2] Select one answer: a) [ 1.0] b) [ 2.0] c) [ 0.25] d) [ 0.5]
d
What is the output of this python code? Code: import numpy as np a = np.arange(5, 0, -1) print a[a < 3] Select one answer: a) [0, 1, 2] b) [5, 4, 3] c) [False, False, False, True, True] d) [2, 1]
d
What does the following code output? Code: import numpy as np syms=['GOOG','AAPL','GLD','XOM'] num_syms = len(syms) allocs = [1.0 / num_syms] * num_syms print allocs Select the answer which the above code will output: a) [0.25, 0.25, 0.25, 0.25] b) [1.0] c) 1.0 d) TypeError: unsupported operand type(s) for /: 'list' and 'float'
a
Section 2
(50 cards)
How should section _A_ filled in to get the below outputs? with extra column (dv) has the sum of the equities each day. Code: import pandas as pd import numpy as np df = pd.DataFrame( data=[[1.0, 2.0, 3.0],[1.0, 2.0, 3.0],[1.0, 2.0, 3.0]] , columns=['GOOG','APPL','XOM'],index=['1/1/2016','1/2/2016','1/3/2016']) print df _A_ print df Output: GOOG APPL XOM dv 1/1/2016 1 2 3 6 1/2/2016 1 2 3 6 1/3/2016 1 2 3 6 Select one answer: A) df['dv'] = np.sum(df,axis=1) B) df += df['dv'].sum(axis=1) C) df['dv'] = df[:,1:3].sum() D) df.columns.add('dv').sum(df,axis=1)
a
import pandas as pd data =[ [ 126.29, 665.41, 409.47, 155.92], [ 126.49, 668.28, 411.67, 156.71], [ 126.82, 659.01, 416.24, 157.78], [ 126.50, 650.02, 420.59, 157.20], [ 126.80, 622.46, 419.93, 156.50], [ 127.90, 623.14, 421.43, 158.64], [ 127.97, 625.96, 420.74, 159.67], [ 128.28, 629.64, 419.59, 160.38] ] cols = ['SPY', 'AAPL', 'GOOG', 'GLD'] dates = pd.date_range('2010-01-01', '2010-01-08') df = pd.DataFrame(data, columns=cols, index=dates) print _A_ Fill the blank (_A_) to get the following output. GLD 2010-01-01 155.92 2010-01-03 157.78 2010-01-05 156.50 2010-01-07 159.67 Select one answer: a) df.ix[:4, 'GLD'] b) df.ix[0:8:2, -1:] c) df.ix[1:8, -1].head(4) d) df['GLD'].head(4)
b
How should section A be filled in to complete code that will cause the following output: Code: import pandas as pd a = pd.DataFrame([1,2,3], columns=['First']) print a b = pd.DataFrame([1,2,3,4], columns=['Second']) print b print __A__ Output: First 0 1 1 2 2 3 Second 0 1 1 2 2 3 3 4 First Second 0 1 1 1 2 2 2 3 3 3 NaN 4 Select one answer: a) a.join(b) b) a.join(b, how='inner') c) a.join(b, how='outer') d) pd.concat([a,b])
c
Fill in the blank for "y" to cause the following output: Code: import numpy as np x = np.random.random([2,5]) print x y = ??? print y Output: [[ 0.05736545 0.66388265 0.90395058 0.9522113 0.92785198] [ 0.77764742 0.25293629 0.27912528 0.98815477 0.10810053]] [[ 0.66388265 0.9522113 ] [ 0.25293629 0.98815477]] Select one answer: a) y = x[:, 1:4:2] b) y = x[1:4:2, :] c) y = x[0:1, 1:2:4] d) y = x[1:2:4, 0:1]
a
How should the blank be filled in to satisfy the mentioned constraint that will cause the following output: Code: import scipy.optimize as spo def f(x): '''Arbitrary function''' n = sum(x**2) return n guess = [0.5, 0.1, 1.2, 0.2] const = _BLANK_ 'Fill in constraint that makes sure the sum of the minimized result adds up to two' result = spo.minimize(f, guess, method = 'SLSQP', constraints=const) print result.x Output: [ 0.5 0.5 0.5 0.5] Select one answer: a) const = ({ 'type': 'eq', 'fun': lambda x: sum(x) == 2}) b) const = ({ 'type': 'eq', 'fun': lambda x: 2 - sum(x)}) c) const = ({ 'type': 'ineq', 'fun': lambda x: sum(x) == 2}) d) const = ({ 'type': 'ineq', 'fun': lambda x: 2 - sum(x)})
b
What would be the output of the following code: Code: import numpy as np a = np.array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9]]) print a[0,:] * sum(a[0:2,-2]) Select one answer: a) [ 5 10 15] b) [ 2 4 6] c) [ 7 14 21] d) [ 7 28 49]
c
How should 'section A' be filled in to complete code that will cause the following output: Code: import pandas as pd d = {'one' : [1., 2., 3., 4.], 'two' : [2., 3., 4., 5.], 'chaz': [3., 10., -2., 0]} df = pd.DataFrame(d) df_dic = {} for i in range(-2,3): df_dic["{0}".format(i)] = df * i print _A_ Output: chaz one two 0 12 4 8 1 40 8 12 2 -8 12 16 3 0 16 20 Select one answer: a) df_dic[1] * 4 b) df_dic[2] c) df_dic['2'] d) df_dic['2'] - df_dic['-2']
d
Question: What is the output of the following python code? import numpy as np arr = np.ones([5,5]) arr[:] = 2 arr[2:] = 3 arr[:, -1] = 4 print arr Choices: A) [[ 2. 2. 2. 2. 4.] [ 2. 2. 2. 2. 4.] [ 3. 3. 3. 3. 4.] [ 3. 3. 3. 3. 4.] [ 3. 3. 3. 3. 4.]] B) [[ 3. 3. 3. 3. 4.] [ 3. 3. 3. 3. 4.] [ 2. 2. 2. 2. 4.] [ 2. 2. 2. 2. 4.] [ 2. 2. 2. 2. 4.]] C) [[ 2. 2. 2. 2. 2.] [ 2. 2. 2. 2. 2.] [ 3. 3. 3. 3. 3.] [ 3. 3. 3. 3. 3.] [ 4. 4. 4. 4. 4.]] D) [[ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1.]]
a
What is the output of this python code? Code: import pandas as pd w = [0, 1, 2] df = pd.DataFrame([[1, 1, 1], [2, 2, 2]], columns=["A", "B", "C"], index=["X", "Y"]) df = df.multiply(w) print df.ix["Y", "C"] Select one answer: a) 4 b) 2 c) 1 d) 0
a
Which is NOT an example of code to replace section _A_ that can calculate the difference between neighboring rows in an ndarray (as illustrated by the output)? import pandas as pd a = pd.Series([1, 2, 4, 7, 11, 16]) _A_ print a print b.values[-1]*1.0 Output: 0 1 1 2 2 4 3 7 4 11 5 16 dtype: int64 5.0 a) b = a-a.shift(1) b=b[1:] b) b=pd.Series([]) for i in range(len(a)-1): b[i] = a[i+1] - a[i] c) b = a-a.shift(1) d) b = a-a.shift(1) b=b[:-1]
d
What is the expected output of the following code? Code: import pandas as pd my_df1 = pd.DataFrame({'X' : ['x1','x2','x3'], 'Y' : ['y1','y2','y3']}) my_df2 = pd.DataFrame({'X' : ['x1','x2','x3'], 'Z' : ['b1','b2','b3']}) my_object = [my_df1, my_df2] my_result = pd.concat(my_object) print(my_result) Select one answer: a) X Y Z 0 x1 y1 NaN 1 x2 y2 NaN 2 x3 y3 NaN 0 x1 y1 b1 1 x2 y2 b2 2 x3 y3 b3 b) ERROR c) X Y Z 0 x1 y1 b1 1 x2 y2 b2 2 x3 y3 b3 d) X Y Z 0 x1 y1 NaN 1 x2 y2 NaN 2 x3 y3 NaN 0 x1 NaN b1 1 x2 NaN b2 2 x3 NaN b3
d
If you want to generate a list that is the length of another list where each entry will have the same value and each value will sum up to one, which of the following will accomplish that. old_l_length = 5 print new_list Output: [0.2,0.2,0.2,0.2,0.2] Select one answer: a) new_list = old_l_length*[1] b) new_list = [(1/h) for h in range(0,1)] c) new_list = old_l_length*[old_l_length] d) new_list = old_l_length*[1.0/old_l_length]
d
By default, numpy's std() function calculates the uncorrected sample standard deviation of a given numpy array. If x is a numpy array of real numbers, then, which of the following lines of code will return the same value as numpy.std(x)? a) sum((x - x.mean())**2) / (len(x)) b) sum((x - x.mean())**2) / (len(x) - 1) c) math.sqrt(sum((x - x.mean())**2) / (len(x))) d) math.sqrt(sum((x - x.mean())**2) / (len(x) - 1))
c
What is the output of this python code? Code: import numpy as np x = np.ones([2,3]) y = [[1,0.5,-0.5],[1,0.5,1]] print 2*(x-y) Select one answer: a) [[ 0. 1. 3.] [ 0. 1. 0.]] b) [[ 0. 1. 4.] [ 0. 1. 0.]] c) [[ 0. 2. 3.] [ 0. 2. 0.]] d)None of the above. The print statement will produce an error since 'x' is of type 'numpy.ndarray' and 'y' is of type 'list'
a
Complete the code below to select first three rows of the dataframe df and divide by the minimum of each column, then print the result. Code: import pandas as pd arr=[[12,4],[6,20],[18,40],[3,16]] df=pd.DataFrame(arr,columns=['A','B']) print ________ Select one answer: a)df[:3,]/df.min(axis=0) b)df[0:2,]/df.min(axis=1) c)df.iloc[:-1,]/df.min(axis=0) d)df.iloc[0:3,]/df.min(axis=1)
c
Given a pandas data frame df, which block of statements will label the x and y axes in the resultant plot? Choose the best answer from those provided: a) df.plot(title='P Values', fontsize=12, x-axis='Date', y-axis='P Value') plt.show() b) df.plot(title='P Values', fontsize=12) df.xaxis = 'Date' df.yaxis = 'P Value' plt.show() c) ax = df.plot(title='P Values', fontsize=12) ax.set_xlabel('Date') ax.set_ylabel('P Value') plt.show() d) df.plot(title='P Values', fontsize=12, xlabel='Date', ylabel='P Value') plt.show()
c
What is the output of this python code? Code: import numpy as np m = np.matrix([[1,1,0,1], [0,0,1,0], [1,1,0,1], [0,1,1,0]]) print m[1:3,-1] + m[-3:-1,1] Select one answer: a) [[1 2]] b) [[1 2 0]] c) [[0] [2]] d) [[0] [2] [1]]
c
How should _A_ be filled to find the index of the column with the largest value Code: import numpy as np x = np.array([[1,9,1],[1,1,1],[1,1,1]]) print x print _A_ Output: [[1 9 1] [1 1 1] [1 1 1]] 1 Select one answer: a) x.argmax(axis=1).max() b) x.argmax(axis=1) c) x[x.max(axis=1)].ix d) np.maximum(x[0],x[1]).max()
a
What is the output of following code? Code: import numpy as np alphabets = np.array(['A', 'B', 'C']) numbers = np.array([[1, 2, 3],[4,5,6],[7,8,9]]) print numbers[alphabets == 'B', 2:] Select one answer: a) [['B']] b) [4,5,6] c) [7,8,9] d) [[6]]
d
In the following program what should be the output marked as _OUTPUT_ below? Code: >>> import pandas as pd, numpy as np >>> cols = ["AGE","HEIGHT", "WEIGHT"] >>> data = [[1,1,None],[2,None,2]] >>> df1 = pd.DataFrame(data, columns=cols) >>> print df1 AGE HEIGHT WEIGHT 0 1 1 NaN 1 2 NaN 2 >>> df1.fillna(20) AGE HEIGHT WEIGHT 0 1 1 20 1 2 20 2 >>> print df1['HEIGHT'].mean() / df1['WEIGHT'].mean() _OUTPUT_ Options: a) NaN b) 0.5 c) 1.0 d) 0.95454545454545459
b
What is the output of the following code? # Credit: borrowed and adapted from the lectures (https://github.com/syednasar/ml4t/blob/master/lessons/1.8%20Optimizers.ipynb) import scipy.optimize as spo def f(x): '''Given a scalar x, return some value''' y = (x-7)**2 + 13 return y x_guess = 2.0 min_result = spo.minimize(f, x_guess, method = 'SLSQP') print "Minimum found at: X = {}, Y = {}".format(min_result.x, min_result.fun) Select one answer: a) Minimum found at: X = [ -7.], Y = [ 13.] b) Minimum found at: X = [ -7.], Y = [-13.] c) Minimum found at: X = [ 2.], Y = [ 38.] d) Minimum found at: X = [ 7.], Y = [ 13.]
d
What is the output of this code: import numpy as np import pandas as pd df = pd.DataFrame([1, 2, 3, 5, 8]) df.apply(lambda x: x ** x) Select one answer: a) 0 0 1 1 4 2 9 3 25 4 64 b) 0 0 1 1 4 2 27 3 3125 4 16777216 c) 0 0 2 1 4 2 6 3 10 4 16 d) 0 0 1 1 2 2 3 3 5 4 8
b
How should section A be filled in to complete code that will cause the following output:
===============CODE================
import numpy as np
import pandas as pd
j = pd.DataFrame(np.random.randn(3, 3),columns=['a', 'b', 'c'])
j.ix[1,:] = np.nan
print "BEFORE:"
print j
j.fillna(method='__A__', inplace="TRUE")
print "
AFTER:"
print j
==============OUTPUT===============
BEFORE:
a b c
0 1.413829 1.752905 -0.597698
1 NaN NaN NaN
2 -0.396044 1.682260 2.131227
AFTER:
a b c
0 1.413829 1.752905 -0.597698
1 -0.396044 1.682260 2.131227
2 -0.396044 1.682260 2.131227
a)forward
b)ffill
c)backward
d)bfill
d
These pieces of python code gives out outcomes as: >>> x = np.arange(1,3).reshape(-1,1) [[1], [2]] >>> a = np.array([1.0, 2.0, 3.0]) >>> b = [2.0] >>> print a * b [ 2. 4. 6.] Then what is the output of this python code? >>> import numpy as np >>> a = np.arange(1,10).reshape(-1,1) >>> b = np.arange(1,10) >>> print a*b Choices: a) [ 1 4 9 16 25 36 49 64 81] b) [[ 1 2 3 4 5 6 7 8 9] [ 2 4 6 8 10 12 14 16 18] [ 3 6 9 12 15 18 21 24 27] [ 4 8 12 16 20 24 28 32 36] [ 5 10 15 20 25 30 35 40 45] [ 6 12 18 24 30 36 42 48 54] [ 7 14 21 28 35 42 49 56 63] [ 8 16 24 32 40 48 56 64 72] [ 9 18 27 36 45 54 63 72 81]] c) [ 9 16 21 24 25 24 21 16 9] d) [285] # np.sum([1, 4, 9, 16, 25, 36, 49, 64, 81]) = 285
b
What is the output of this python code? code: import numpy as np xxarray = np.array([[3,4,5],[6,7,8]]) print xxarray[::-1,::-1] Select one answer: a) 8 b) [[5 4 3] [8 7 6]] c) [[8 7 6] [5 4 3]] d) [8 7 6]
c
What is the output of the following code? Code: import numpy as np data = np.array([ [2.0, 4.0, 8.0], [1.0, 2.0, 4.0], [4.0, 8.0, 16.0]]) output = data.sum(axis=1) print(output) Choices: A) 49. B) [7., 14., 28.] C) 14. D) [14., 7., 28.]
d
How should section A be filled in to complete code that will cause the following output: Code: pts = [{'x': 0, 'y': 0, 'z': 1}, {'x': 2, 'y': 0, 'z': 1}, {'x': 3, 'y': 3, 'z': 1}] df = pd.DataFrame(pts) print df _A_ print df Output: x y z 0 0 0 1 1 2 0 1 2 3 3 1 x y z 0 0 0 1 1 4 0 1 2 6 6 1 Select one answer: a) df = df*3 b) df.ix[:, 0:1] = df.ix[:, 0:1]*2 c) df.ix[0:2, 0:3] = df.ix[:, 0:2]*2 d) df.ix[:, 0:2] = df.ix[:, 0:2]*2
d
series1 = [1,2,3,4,5,6,7,8,9] print (series1[1:2],series1[5:7]*2) What is the output of this python code? a). ([2], [12, 14]) b). ([2], [6, 7, 6, 7]) c). ([2, 3], [6, 7, 8, 6, 7, 8]) d). ([2, 3], [12, 14, 16])
b
Given 100 days of data (sampled daily), which is the proper python formula to calculate sharpe ratio? Assume sr is Sharpe Ratio, dr is daily return, rfr is risk-free-rate and np is a numpy library. a. sr = np.sqrt(252) * (dr - rfr).mean()/dr.std() b. sr = np.sqrt(252) * np.std(dr - rfr)/np.mean(dr -rfr) c. sr = np.sqrt(252) * np.mean(dr - rfr)/np.std(dr) d. sr = np.sqrt(100) * (dr - rfr).mean()/dr.std()
a
What is the output of the following code? import numpy as np a = np.array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12], [13, 14, 15, 16]]) print a[-1:,1:3] Select one answer from the following options: (a) [10 11] (b) [14 15] (c) [14 15 16] (d) [13 14 15 16]
b
What is the output of the following code? import numpy as np import pandas as pd a = np.array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9]]) df = pd.DataFrame(a) df.columns = [['N1', 'N2', 'N3']] print(df['N2'][1:3].sum()) Select one answer: a) 11 b) 13 c) 15 d) 9
b
Which is the correct code for Section _A_ to set the start date values in a Pandas Datafame to 0 assuming there are two or more securities? dates = pd.date_range(dt.datetime(2005,01,01), dt.datetime(2005,01,05)) # data_frame is a Pandas Data Frame retrieved using the util.py helper function data_frame = get_data(['GOOG','AAPL','GLD'], dates) #set first trading day in series to 0 _A_ print data_frame Output: SPY GOOG AAPL GLD 2005-01-03 0.00 0.00 0.00 0.00 2005-01-04 102.65 194.50 31.83 42.74 2005-01-05 101.94 193.51 32.11 42.67 Select one answer: a) data_frame.ix[:,0] = 0 b) data_frame.ix[0,:] = 0 c) data_frame[0,:] = 0 d) data_frame[:,0] = 0
b
What is the output of this python code? Code: import numpy as np A = np.arange(0, 100, 10) B = A[[2, 4, 6]] C = - A[[2]] res = B - C print res Select one answer: a) [0 2 4] b) [4 6 8] c) [40 60 80] d) [20 40 60]
c
What is the output of this python code? Code: array_1 = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24], [25, 26, 27, 28, 29]] array_2 = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] df = pd.DataFrame(array_1, columns=list('abcde')) x = np.array(array_2) df = df.ix[2:2, ['a', 'b']] * x[-1, 1:5:2] print df.get_values() Select one answer: a) [[25 42]] b) [[60 88]] c) [[50 77]] d) Error message
b
What is the output of the following script: Code: import pandas as pd df = pd.DataFrame(data={'price':[1,2,3,4,5]}, index=pd.date_range('2010-01-01','2010-01-05')) pd.rolling_mean(df, window=2) Select one answer: a) price 2010-01-01 1.0 2010-01-02 1.5 2010-01-03 3.0 2010-01-04 4.5 2010-01-05 6.0 a) mean 1 1.5 2 2.5 3 3.5 4 4.5 c) ValueError: min_periods must be >= 0 d) price 2010-01-01 NaN 2010-01-02 1.5 2010-01-03 2.5 2010-01-04 3.5 2010-01-05 4.5
d
What is the output of the following python code? Code: import numpy as np a = np.array([2, 7, 11, 9, 3]) a[a < 5] = 5 a[2] = 10 print a Select one answer: a) [ 2 5 10 5 3] b) [ 5 7 10 5 3] c) [ 5 7 10 9 5] d) [ 5 10 11 9 5]
c
How should section A be filled to print out only the first two values in the array as seen in the sample output below: Which of the following returns the last five values of the array Code: import numpy as np a = np.random.random([5]) print a print _A_ Output: [ 0.70059652 0.98449675 0.75068418 0.31808623 0.14485409] [ 0.70059652 0.98449675] Select one answer: a) a[-2] b) a[:-2] c) a[-5:-3] d) a[2:-1]
c
What is the output of the following python code? Code: import numpy as np array = np.random.randint(4, 5, size = (6, 7)) print array.shape[0] Select one answer: a) 4 b) 5 c) 6 d) 7
c
What is the output of this python code? Code: import numpy as np a = np.array([[1,2,3],[4,5,6],[7,8,9]]) a[0:3,2] Select one answer: a) 24 b) array([7, 8, 9]) c) IndexError: index 3 is out of bounds for axis 0 with size 3 d) array([3, 6, 9])
d
What is the output of the following code? Code: import numpy as np x = np.array([5,4,3,2,1]) print x[-1:] * x[:-1] Select one answer: a) [20 12 6 2] b) [5 8 9 8] c) [10 12 12 10] d) [5 4 3 2]
d
Given following allocations: allocs = [0.1,0.2,0.3,0.4] Which of the following code will reverse the allocations? a) allocs = allocs[::-1] b) allocs = allocs * -1 c) allocs = allocs[-1::] d) allocs = allocs[:-1:]
a
What is the output of this python code? array = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) array = array[:3, :3] array = array[1:, 1:] print(array) Options a) [[ 6, 7, 8] [10, 11, 12] [14, 15, 16]] b) [[ 11 ]] c) [[ 6, 7] [10, 11]] d) [[ 0, 0, 0, 4] [ 0, 0, 0, 0] [ 0, 0, 0, 0] [13, 0, 0, 0]]
c
Was this output created by A, B, C, or D? 0 1 2 2 4 # A print [[' ', 0], [1, 2], [2, 4]] # B import pandas as pd print pd.DataFrame([2, 4], columns=[0]) # C import pandas as pd print pd.DataFrame([[1, 2], [2, 4]], columns=[' ', 0]) # D import pandas as pd print pd.DataFrame([2, 4], index=(1, 2))
d
What is the output of the following code:
import numpy as np
a=np.array([(10.0,20.0),(1.0,2.0)])
b=np.array([(100,200),(1,2)])
print "
Divide a by b:
", a/b
Select one answer:
a) Divide a by b:
[[0 0]
[1 1]]
b) Divide a by b:
[[ 0.1 0.1]
[ 1. 1. ]]
c) Divide a by b:
[[0.0 0.0]
[1.0 1.0]]
d) Divide a by b:
[[10 10]
[.10 .10]]
b
What output does the following code snippet produce? def my_func(x, y=-1): return x ** 2 + y print my_func(3) + my_func(6, 5) A. 25 B. 45 C. 50 D. 49
d
Choose the correct output of the print statement of the following code: import numpy as np C = np.ndarray([2,2], buffer=np.matrix([[1, 2], [3, 4]]), dtype=int) print C[:,0:1] Select one answer: a) [1 3] b) [[1] [3]] c) [[1 2] [3 4]] d) [[1 2]]
b
Code: import numpy as np a = np.arange(0,9).reshape((3,3)) print a print __A__ Output: [[0 1 2] [3 4 5] [6 7 8]] [0] Select one answer: a) a[0] b) a[a>1] c) a[-1] d) a[a==0]
d
How should section A be filled in to complete code that will cause the following output: Code: print _A_ Output: ((0.0, 1.0), (0.0, 1.0), (0.0, 1.0)) Select one answer: a) ((0., 1.),)*3 b) [(0., 1.) for i in [0,0,0]] c) (0., 1.)*3 d) [(0., 1.) for i in 3]
a
What is the output of the following code? Code: import numpy as np arr = np.arange(10) arr_slice = arr[3:5] arr_slice[:] = 4 temp_number = 1 arr_slice = temp_number print arr Select one answer: a) [1 2 3 4 4 6 7 8 9 10] b) [0 1 2 4 4 5 6 7 8 9] c) [1 2 3 1 6 7 8 9 10] d) [0 1 2 3 4 5 6 7 8 9]
b
Given a dataframe df depicting the daily returns of a portfolio, describe the output of the print statement below: Code: import pandas as pd data = [1.0, 1.05, 1.2, 0.99, 1.5] # create dates starting Jan 1st and ending Jan 5th ind = pd.date_range(start='2016-01-01', end='2016-01-05') name = ['daily_returns'] df = pd.DataFrame(data=data, index=ind, columns=name) a = df['daily_returns'] - df['daily_returns'].mean() b = (a**2).sum() c = df.shape[0]-1 # what is the output of the print statement below? print (b/c)**0.5 a) The sample standard deviation of daily_returns b) The population standard deviation of daily_returns c) The sum of squared error of daily_returns d) The root mean squared error of daily_returns
a
Section 3
(50 cards)
You want to compare the evolution of the value of each column. How should you normalize a dataframe by column so that the first row is all ones ? Code : import numpy as np import pandas as pd df = pd.DataFrame(np.random.random([3,3])) print _A_ Output : 0 1 2 0 1.000000 1.000000 1.000000 1 31.028261 0.520715 11.288351 2 0.313378 0.938353 12.367858 Select one answer: a) df/df[:].max() b) df/df.ix[0] c) df/df.ix[:,0] d) df.ix[:,0]=[1,1,1]
b
Fill the blank in above to cause this Python code to give the following output. You are given a Pandas Dataframe 'df', containing 10 stocks from 1st Jan 2009 to 31st Dec 2009, and you are required to select data of 'AAPL' and 'GLD' for last five days. Output: " AAPL GLD 2009-12-27 904 884 2009-12-28 75 172 2009-12-29 381 4 2009-12-30 892 92 2009-12-31 417 169 " Select one answer: a) df.ix[-1:-5,['AAPL','GLD']] b) df.ix[-5:-1, ['AAPL', 'GLD']] c) df[-5:, ['AAPL', 'GLD']] d) df.ix[-5:, ['AAPL', 'GLD']]
d
Given the following code: import numpy as np a=np.array([(1.,2.,3.),(1.,2.,3.),(1.,2.,3.)]) which line of code produces the following output?: [ 4. 4. 4.] a) print (a*2).min(axis=0) b) print (a*2).max(axis=1) c) print (a*2).mean(axis=0) d) print (a*2).mean(axis=1)
d
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np a = np.array([[1,2,3,4,5], [11,12,13,14,15], [21,22,23,24,25], [31,32,33,34,35], [41,42,43,44,45]]) b = np.array([[51,52,53,54,55]]) print _A_ Output: [[ 1 2 3 4 5 51] [11 12 13 14 15 52] [21 22 23 24 25 53] [31 32 33 34 35 54] [41 42 43 44 45 55]] Select one answer: a) np.concatenate((a, b), axis=1) b) np.concatenate((a, b.T), axis=0) c) np.concatenate((a, b.T), axis=1) d) np.concatenate((a, b), axis=0)
c
What is the output of the following code? import numpy as np j = np.array([[4,1],[5,5],[1,4]], np.float64) nm = j/j.sum(axis=0) pt = nm*100 print pt Select one answer: a) [[ 80. 20.] [ 50. 50.] [ 20. 80.]] b) [[ 40. 10.] [ 50. 50.] [ 10. 40.]] c) [[ 20. 5.] [ 25. 25.] [ 5. 20.]] d) [[ 4. 1.] [ 5. 5.] [ 1. 4.]]
b
What code would you put in for _blank_ to produce the console output produced by this method? def normalize(): prices = np.array([(5.19,8.5),(5.1,8.6),(5.15,8.7),(5.3,8.8),(5.25,8.9)]) df = pd.DataFrame(prices, columns=['SYM1','SYM2']) print df / _blank_ Console Output: SYM1 SYM2 0 1.000000 1.000000 1 0.982659 1.011765 2 0.992293 1.023529 3 1.021195 1.035294 4 1.011561 1.047059 a) df.ix[0,:] b) df.ix[head] c) df.head() d) df[0,:]
a
What would be the output of the following Python code: import numpy as np A = np.array([[1.,3.,5.,7.],[2.,4.,6.,8.]]) B = A[:,::2] C = B.mean(axis=1) print C Select one answer: a) [3., 4.] b) [1.5, 5.5] c) [3.5] d) [4., 5.]
a
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np j = np.array([[0., 1., 2.],[3., 4., 5.],[6., 7., 8.]]) print j print j/j[1,:] Output: [[ 0. 1. 2.] [ 3. 4. 5.] [ 6. 7. 8.]] [[ 0. 0.25 0.4 ] [ 1. 1. 1. ] [ 2. 1.75 1.6 ]] Select one answer: a) j / j[0,0] b) j / j[:,1] c) j / j.sum(axis = 1) d) j / j[1,:]
d
How should section A be filled in to complete the code so that the last "print df" will generate the following output: Code: import numpy as np import pandas as pd rand = np.random.randint(5, size=(3,3)) df = pd.DataFrame(rand) print "=== original dataframe ===" print df print "=== modified dataframe ===" _____A_____ print df Output: ===original dataframe=== 0 1 2 0 4 4 4 1 4 1 2 2 1 2 0 ===modified dataframe=== 0 1 2 0 4 16 4 1 4 1 2 2 1 4 0 Select one answer: a) df.ix[:,1]^2 b) df.ix[:,1]**2 c) df.ix[:,1] = df.ix[:,1]^2 d) df.ix[:,1] = df.ix[:,1]**2
d
What is the output of the following code? Code: import numpy as np a = np.array([[1,2,3], [3,4,5], [4,5,6]]) print a[:,1:] Select one answer: a) [[1,2,3] [3,4,5]] b) [[3,4,5] [4,5,6]] c) [[2 3] [4 5] [5 6]] d) [[1,2] [3,4] [4,5]]
c
QUESTION CODE: import numpy as np import pandas as pd df = pd.DataFrame(np.random.random_integers(1,50,size=(10, 3)), columns=['col1', 'col2', 'col3']) print df x = df[6:10] y = x/x __A__ df1 = pd.concat(dframes) print df1 col1 col2 col3 0 19 12 37 1 11 21 8 2 41 36 10 3 27 37 29 4 32 34 6 5 31 48 45 6 39 48 28 7 30 14 27 8 9 24 44 9 32 14 1 What code in __A__ will produce the output col1 col2 col3 0 19 12 37 1 11 21 8 2 41 36 10 3 27 37 29 4 32 34 6 5 31 48 45 6 1 1 1 7 1 1 1 8 1 1 1 9 1 1 1 A. dframes = [df[1:6], y] B. dframes = [df[1:7], y] C. dframes = [df[0:7], y] D. dframes = [df[0:6], y]
d
def pandas_only_daterange(df, bd, ed): ''' INPUT: DataFrame, High, Low, Close prices OUTPUT: DataFrame Return a new pandas DataFrame which contains the entries for the provided date range ''' (a) df.ix([bd:ed]) (b) df[bd:ed] (c) df.iloc[bd:ed] (d) df.index([bd:ed])
b
If adr = Average Daily Returns and sddr = Volatility, what is the correct formula to calculate the sr (Sharpe Ratio) is section A: Code: def test_run(): dates = pd.date_range('2009-01-01', '2012-12-31') symbols = ['SPY'] df = get_data(symbols, dates) daily_returns = compute_daily_returns(df) adr=daily_returns['SPY'].mean() sddr=daily_returns['SPY'].std() sqrt_frf = 15.87450786638754 sr = _A_ print "Sharpe Ratio:", sr Output: Sharpe Ratio: 0.754609034965 Select one answer: a) sr = adr / (sqrt_frf * sddr) b) sr = sddr / (sqrt_frf * adr) c) sr = sqrt_frf * (adr / sddr) d) sr = sqrt_frf * (sddr / adr)
c
Given the following python code, what will the values of x and y be after execution: import numpy as np tmparr = np.array( [[ 1, 2, 3], [ 2, 1, 4]]) x = np.sum(tmparr , axis=1)[0] y = x / tmparr[-1,-1] Select one answer: a) x: 3 y: 4 b) x: 3 y: 0.75 c) x: 6 y: 1 d) x: 6 y: 1.5
c
Type 1: What is the output of the following python code? Code: import numpy as np import pandas as pd array = np.array([[1,2,3,4,5],[6,7,8,9,0],[0,9,8,7,6],[5,4,3,2,1]]) df=pd.DataFrame(array,columns=['C1', 'C2', 'C3', 'C4', 'C5']) print df.ix[1:3,['C2','C4']] a)7 9 9 2 b) C2 C4 1 7 9 2 9 7 3 4 2 c) C2 C4 9 7 7 9 2 4 d) C2 C4 1 7 9 2 9 7
b
In [1]: import numpy as np In [2]: r = np.random.rand(4) In [3]: print r Select one answer: a) [ 0.00000001, 0.98765443, 0.00084734, 0.23423342 ] b) [ 0.00000001, 0.98765443, 0.00084734, -0.23423342 ] c) [ 0.00000001, 0.98765443, 0.00084734, 1.23423342 ] d) [ 0.00000001, 0.98765443, 0.00084734, 1.00000000 ]
a
What is the output produced by the following code segment? import pandas as pd s1 = pd.Series((2, 5, 7)) s2 = pd.Series((3, 8, 2)) s3 = pd.Series((5, 9, 4)) s4 = pd.Series((4, 7, 3)) df = pd.DataFrame([s1 + s2 + s3 + s4]) print df a) 0 0 14 1 13 2 18 3 14 b) 0 1 2 0 14 29 16 c) 0 0 59 d) [[2, 5, 7], [3, 8, 2], [5, 9, 4], [4, 7, 3]]
b
How should section A be filled in to complete code that will cause the following output: THE CODE: import pandas as pd numbers= pd.DataFrame([[8, 4, 1], [2, 5, 7], [6, 2, 3]]) numbers= numbers/__A__ print numbers THE OUTPUT: 0 1 2 0 1.00 1.00 1 1 0.25 1.25 7 2 0.75 0.50 3 SELECT ONE ANSWER: a) numbers[0,:] b) numbers[:,0] c) numbers.ix[0,:] d) numbers.ix[:,0]
c
Data is selected for 10 stocks: ['GOOG', 'AAPL', 'XON', 'SPY', 'GLD', 'IBM', 'BUD', 'CBG', 'KIJ', 'LMN'] in a specific range[2009-2010]. And, question asks the student to select APPL and GLD stocks for the last 5 records. """ import pandas as pd import numpy as np a = np.random.random_integers(1000, size=(365., 10.)) dates = pd.date_range('2009-1-1', periods=365, freq='D') df = pd.DataFrame(a) df.index = dates df.columns = [['GOOG', 'AAPL', 'XON', 'SPY', 'GLD', 'IBM', 'BUD', 'CBG', 'KIJ', 'LMN']] #a #print df.ix[:-5,['AAPL','GLD']] #b #print df.ix[-5:-1, ['AAPL', 'GLD']] #c #print df[-5:, ['AAPL', 'GLD']] #d #print df.ix[-5:, ['AAPL', 'GLD']]
d
If you needed to compute the daily returns of a given dataframe, and subsequently set the first rows for each column/stock to 0.0, which of the following code snippets should you use? NOTE: "df" is a single-column dataframe containing the properly calculated portfolio values for each day. The format for df looks like: 2010-01-04 1000000.000000 2010-01-05 994880.982851 2010-01-06 995136.933709 2010-01-07 993601.228654 Freq: D, dtype: float 64 NOTE: there is extra emphasis on the term "should" - this indicates using the proper code to ensure the initial dataframe does not change (i.e. there is only one answer that is the optimal choice that ensures the original state of the passed in dataframe is not changed) import pandas as pd import numpy as np def get_daily_returns(df): <CODE SNIPPET HERE> A) dr = df.copy() dr[:1] = (df[1:] / df[-1:].values) - 1 dr[0] = 0.0 return dr B) dr[1:] = (df[1:] / df[:-1].values) - 1 dr[0] = 0.0 return dr C) dr = df.copy() dr[1:] = (df[1:] / df[:-1].values) - 1 dr[0] = 0.0 return dr D) dr = df.copy() dr[1:] = (df[1:] / df[-1:]) -1 dr[1:] = 0.0 return dr
c
import pandas as pd import numpy as np test_frame = pd.DataFrame(np.random.rand(8,4), index=list('abdcefgh'), columns=['st1','st2','st3','st4']) print 'Test Frame' print test_frame print 'Mean for first 4 Rows of st2' print __ANSWER__.mean() print 'Standard Deviation for first 4 Rows of st2' print __ANSWER__.std() Output: Test Frame st1 st2 st3 st4 a 0.338524 0.126643 0.776153 0.610379 b 0.262568 0.103882 0.995729 0.207025 c 0.067094 0.394764 0.970601 0.282487 d 0.690560 0.573392 0.288221 0.061491 e 0.762193 0.679864 0.241871 0.223460 f 0.318419 0.167459 0.411278 0.862037 g 0.821099 0.390488 0.772339 0.890881 h 0.905461 0.891842 0.181957 0.471498 Mean for first 4 rows of 0.20842977928 Standard Deviation for first 4 Rows of st2 0.161771239476 #Select one asnwer: #a) df.ix[:3, 'st2'] #b) df['st1'] #c) df.iloc[:3, 'st2'] #d) df.ix[0]
a
Which of the following answers is the correct output? import pandas as pd import numpy as np df = pd.DataFrame([[1,2,3,4,5],[6,7,8,9,10],[2,3,4,5,6],[7,8,9,10,11],[0,1,0,1,0]]) a = np.arange(4,0,-2) df1 = df.ix[-3:,a] print df1 Options a) 4 2 2 6 4 3 11 9 4 0 0 b) Empty DataFrame Columns: [] Index: [0, 1] c) 4 2 0 5 3 1 10 8 2 6 4 3 11 9 4 0 0 d) 2 3 4 4 0 1 0 2 4 5 6
c
Choose the correct line of code to print the output shown below: Code: import numpy as np import pandas as pd df1 = pd.DataFrame(np.random.randn(10,3)) print df1 [ chosen code goes here ] Output: 0 1 2 0 1.629342 -1.081380 -0.790401 1 2.292899 -0.628032 -0.101210 2 0.562970 -1.014486 0.734165 3 0.912935 1.483613 -0.035802 4 1.456115 1.320073 1.736249 5 -1.250448 0.423665 1.689530 6 1.015360 1.011011 0.181923 7 0.396016 -1.921691 0.207542 8 -1.221396 -0.433596 0.806824 9 1.114499 0.457012 0.433003 7 0.396016 8 -1.221396 9 1.114499 Name: 0, dtype: float64 Select one answer: a) print df1[3].tail(0) b) print df1[0].tail(3) c) print df1[:3] d) print df1[3:]
b
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np a = np.arange(16).reshape(4,4) print a print _A_ Output: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] [24 28 32 36] Select one answer: a) a.sum(axis=0) b) a[3,:] c) a.sum(axis=1) d) a[:,3]
a
What is the output of the following code: import numpy as np a = np.array([(12,20,8,15,6,14),(18,22,0,8,19,15)]) a[a>15] = 15 print a Select one answer: a) [[12 20 8 15 6 14] [18 22 0 8 19 15]] b) [[15 15 15 15 15 15] [15 15 15 15 15 15]] c) [[12 15 8 15 6 14] [15 15 0 8 15 15]] d) [[12 20 8 15 6 14] [18 22 0 8 19 15]]
c
We desire to achieve Naive Scalar multiplication by two (two times the input object vector range) Input range: v = [1, 2, 3, 4, 5] Desired Output (twice the input): [2, 4, 6, 8, 10] Which program results in correct answer: A: v = range(1, 6) print 2 * v B: import numpy as np v = np.arange(1, 6) print 2 * v Answers: 1: A 2: B 3: Both A & B 4: Neither A & B
b
What is the output of the following code executed in a Python console: Code: import numpy as np def foo(x): return 1 + x ** 2 a = np.array([0.2, 0.2, 0.2, 0.2, 0.2]) b = [0.2, 0.2, 0.2, 0.2, 0.2] print(foo(a)) print(foo(b)) Select one answer: a) [ 1.04 1.04 1.04 1.04 1.04] [ 1.04 1.04 1.04 1.04 1.04] b) [ 1.04 1.04 1.04 1.04 1.04] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 2, in foo TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int' c) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 2, in foo TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int' Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 2, in foo TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int' d) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 2, in foo TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int' [ 1.04 1.04 1.04 1.04 1.04]
b
How should section A be filled in to complete code that will cause the following output: Code: import numpy as np j = np.array([[2,3],[1,1]]) k = np.array([[3,3],[3,3]]) print j print k print _A_ Output: [[2 3] [1 1]] [[3 3] [3 3]] [[15 15] [ 6 6]] Select one answer: a) np.dot(j, k) b) np.multiply(j, k) c) j * k d) j / k
a
What is the output of this python code? Code: import numpy as np x = np.array([[1,2,3],[3,4,5],[5,6,7]]) print x[1]+x[-1] Output: ? Select one answer: a) [4 8 12] b) [5 9 13] c) [8 10 12] d) [6 8 10]
c
Given >>> import pandas as pd >>> project1_scores = pd.DataFrame([92.0, 87.0, 95.0], index=['Janet', 'Ariel', 'Laurel'], columns=['Project 1']) >>> project2_scores = pd.DataFrame([89.0, 98.0, 90.0], index=['Ariel', 'Laurel', 'Boyd'], columns=['Project 2']) How should section X and Y be filled in to complete code that will cause the following output: >>> combined_scores = project1_scores.join(project2_scores, how= X ) >>> print combined_scores Project 1 Project 2 Ariel 87 89 Boyd NaN 90 Janet 92 NaN Laurel 95 98 >>> ariels_score = Y >>> print ariels_score Project 1 87 Project 2 89 Name: Ariel, dtype: float64 Select one answer: a) X 'inner', Y combined_scores.ix['Ariel'] b) X 'inner', Y combined_scores[0] c) X 'outer', Y combined_scores.ix['Ariel'] d) X 'outer', Y combined_scores[0]
c
What is the output of the following code? Code: import numpy as np a = np.matrix([[0,1,2,3], [4,5,6,7], [8,9,10,11], [12,13,14,15]]) a = a[::-1,::-1] print a[1:3,1:] Select one answer: a) [[15 14 13] [11 10 9]] b) [[10 9 8] [ 6 5 4]] c) [[10 9] [ 6 5] [ 2 1]] d) [[ 5 6] [ 9 10] [13 14]]
b
What does the last line of the following code output: Code: import pandas as pd from util import get_data dates = pd.date_range('2010-08-23', '2010-08-26') symbols = ['IBM', 'GOOG', 'GLD', 'XOM'] df = get_data(symbols, dates) print df print df[['GOOG', 'GLD', 'XOM']][1:3] # What does this line output? Output: IBM GOOG GLD XOM 2010-08-23 122.30 464.07 119.78 56.68 2010-08-24 120.78 451.39 120.36 56.15 2010-08-25 121.14 454.62 121.36 56.12 2010-08-26 118.73 450.98 120.96 55.71 # # WHAT ELSE HERE? # Select one answer: a) GOOG GLD XOM 2010-08-24 451.39 120.36 56.15 2010-08-25 454.62 121.36 56.12 b) IBM GOOG GLD XOM 2010-08-24 120.78 451.39 120.36 56.15 2010-08-25 121.14 454.62 121.36 56.12 c) GOOG GLD XOM 2010-08-24 451.39 120.36 56.15 2010-08-25 454.62 121.36 56.12 2010-08-26 450.98 120.96 55.71 d) GOOG GLD XOM 2010-08-24 451.39 120.36 NaN 2010-08-25 454.62 121.36 56.12
a
What is the output of the following code? import numpy as np x= np.array([[1,2,3],[4,5,6], [7,8,9]]) y = np.amax(x, axis=1) z = x/y print z (a) [[0, 0, 0], [1, 0, 0], [2, 1, 1]] (b) [[ 0.33333333, 0.33333333, 0.33333333], [ 1.33333333, 0.83333333, 0.66666667], [ 2.33333333, 1.33333333, 1. ]] (c) [[0, 0, 0], [0, 0, 0], [1, 1, 1]] (d) [[ 0.14285714, 0.25 , 0.33333333], [ 0.57142857, 0.625 , 0.66666667], [ 1. , 1. , 1. ]]
a
What is the output of the python code below? Code: import numpy as np a = np.array([10,30,50,70]) b = a / a[0] print b[-1] Select one answer: a) IndexError b) 10 c) 5 d) 7
d
How should you transform numpy array A into numpy array B, which basically subtract the mean of each row of a matrix. Please use the numpy built-in operation. A = [[ 1. 2. 3.] [ 0. 10. 20.] [ 3. 4. 5.]] B = [[ -1., 0., 1.], [-10., 0., 10.], [ -1., 0., 1.]] So B = ? Select one answer: a)A - A.mean(axis = 1) b)A - A.mean(axis = 1, keepdims = True) c)A - A.mean(axis = 0) d)A - A.mean(axis = 0, keepdims = True)
b
How should the TODO section be filled in to cause the following output: Code: import numpy as np myArray = np.random.randint(0,10,size=(4,4)) print "Before..." print myArray print "After..." #TODO - add code here print myArray Output: Before... [[3 6 6 2] [0 3 6 3] [7 0 8 3] [7 9 7 5]] After... [[3 6 6 2] [0 3 6 3] [7 0 8 3] [3 6 6 2]] Select one answer: a) myArray[0:1] = myArray[3:4] b) myArray[0:1,] = myArray[-1:,] c) myArray[-1:,] = myArray[0:1,] d) myArray[-4:,] = myArray[0:4,]
c
How should section A be filled in to complete code that will cause the following output: Code: import pandas as pd prices = pd.DataFrame([[185.35, 4.71, 113.83],[186.27, 4.83, 112.97],[184.22, 4.97, 112.33]],columns=['SPY','OIL', 'GLD']) print prices print _A_ Output: SPY OIL GLD 0 185.35 4.71 113.83 1 186.27 4.83 112.97 2 184.22 4.97 112.33 SPY OIL GLD 1 185.35 4.71 113.83 2 186.27 4.83 112.97 Select one answer: a) prices.shift(1)[0:] b) prices.shift(1)[:] c) prices.shift(1)[1:] d) prices.shift(-1)[1:]
c
What is the output of this python code? Code: import numpy as np a = np.arange(6) a = a.reshape(3,2) print a.min(axis=1) Select one answer: a) 0 b) [0 1] c) [0 2 4] d) [1 2]
c
You are working on a project that involves time series data (time on one axis, value on the other) for a biological experiement involving bacterial growth. Unfortunately, the lab technician on your team is somewhat of a Python beginner. They have written a function, getData(), that takes the name of a given bacterial species as a string input and returns a one-dimenstional numpy ndarray of that bacterial species' population per time point, but without any time labels. Your teammate does, however, inform you that all measurements were taken once per hour for 12 hours starting at t=0 hrs, such that there are 13 entries in each array. How would you complete the following code to construct a pandas dataframe with columns corresponding to the bacterial species, and rows corresponding to the measurement times? Code: ======================================================================================= import numpy as np import pandas as pd import random # teammate's code. generates synthetic data. see note at bottom of this document. bacterial_species = ['eColi', 'cDiff', 'staphA', 'gNeg', 'strep'] bacterialData = {} for bs in bacterial_species: seed = random.randint(0, 1000) time = np.arange(0, 13, 1) bacterialData[bs] = np.asarray([seed2*t for t in time]).astype('float') def getData(s, data=bacterialData): return data[s] # your code bacterial_species = ['eColi', 'cDiff', 'staphA', 'gNeg', 'strep'] time_points = np.arange(0, 13, 1) df = pd.DataFrame(index=time_points, columns=bacterial_species) for bs in bacterial_species: population = getData(bs) ## what line should go here? ## print df Output: ======================================================================================= eColi cDiff staphA gNeg strep 0 763 920 175 598 58 1 1526 1840 350 1196 116 2 3052 3680 700 2392 232 3 6104 7360 1400 4784 464 4 12208 14720 2800 9568 928 5 24416 29440 5600 19136 1856 6 48832 58880 11200 38272 3712 7 97664 117760 22400 76544 7424 8 195328 235520 44800 153088 14848 9 390656 471040 89600 306176 29696 10 781312 942080 179200 612352 59392 11 1562624 1884160 358400 1224704 118784 12 3125248 3768320 716800 2449408 237568 Select one answer: ======================================================================================= a) df.ix[bacterial_species.index(bs), :] = population b) df[:, bacterial_species.index(bs)] = population c) df[[bs]] = population d) df[bs] = population
d
The numpy array below contains closing prices for six securities over a ten day period. What is the output of this python code? Code: import numpy as np # SPY IBM AAPL HNZ XOM GLD prices = np.array([[ 86.8 , 81.64, 90.36, 33.95, 74.48, 86.23], [ 86.7 , 81.13, 94.18, 33.82, 74.47, 84.48], [ 87.28, 83.38, 92.62, 33.38, 73.26, 85.13], [ 84.67, 82.03, 90.62, 32.59, 71.39, 82.75], [ 85.01, 81.46, 92.3 , 31.99, 72.15, 84.46], [ 83.19, 79.15, 90.19, 31.69, 70.77, 83.92], [ 81.19, 80.09, 88.28, 31.49, 69.83, 80.76], [ 81.34, 79.74, 87.34, 31.75, 71.09, 80.88], [ 78.78, 77.74, 84.97, 30.65, 68.51, 79.79], [ 78.81, 78.6 , 83.02, 30.67, 69.94, 80.39]]) print prices[:,-1] ### what is the output of this??? Select one answer: a) A list of closing prices of all six securities on the 10th day b) 80.39 (The closing price of GLD on the 10th day) c) A list of the closing price of GLD for all ten days d) It does not actually output any prices; it actually generates an IndexError exception
c
A numpy 2d array contains this value [[5,3],[10,2]]. Please fill in section X to get the following output:
import numpy as np
a = np.array([[5,3],[10,2]])
print "Input:
", a
print "
Output:
", X
Input:
[[ 5 3]
[10 2]]
Output:
[[15 13]
[20 12]]
a) X = a + np.max(a[0,:])
b) X = a + np.max(a[:,1])
c) X = a + np.max(a[:,:])
d) X = a + np.min(a[:,:])
c
What is the output from the following python code? Code: import numpy as np m = np.array([[4,9,16],[25,36,49],[64,81,100]]) m = m / np.sqrt(m) print(m) Select one answer: a) [[ 0.20412415 0.45927933 0.81649658] [ 1.27577591 1.83711731 2.50052078] [ 3.26598632 4.13351394 5.10310363]] b) [[ 2. 3. 4.] [ 5. 6. 7.] [ 8. 9. 10.]] c) 2. 3. 4. 5. 6. 7. 8. 9. 10. d) [[ 16 81 256] [ 625 1296 2401] [ 4096 6561 10000]]
b
What is the output of this python code? import numpy as np alana_array = np.array([[22, 7, 8], [45, 1, 1], [123, 7, 45], [20, 0, 14]]) print alana_array.shape[1] A) 3 B) 4x3 C) 3x4 D) 4
a
What is the output of the following code? Code: import numpy as np a=np.array([[0, 1, 0],[2, 0, 3],[0, 4, 0]]) print a.max(axis=0).min() Select one answer: a) 1 b) 2 c) 3 d) 4
b
Given the following python code fragment,
import pandas as pd
from util import get_data
dates = pd.date_range("2010-01-06", "2010-01-07")
symbols = [ 'IBM', 'GOOG', 'AAPL', 'XOM', 'HP' ]
symbols = [ w for w in sorted(symbols[1:]) ]
prices = get_data(symbols,dates)
sym = symbols[-1]
print "sym = {}
{}".format(sym,prices[sym])
#-------------------------------------------------------------------------------
What would be the output?
(a)
sym = AAPL
2010-01-06 210.07
2010-01-07 209.68
Freq: D, Name: AAPL, dtype: float64
(b)
sym = XOM
2010-01-06 65.36
2010-01-07 65.15
Freq: D, Name: XOM, dtype: float64
(c)
sym = IBM
2010-01-06 123.90
2010-01-07 123.47
Freq: D, Name: IBM, dtype: float64
(d)
sym = HP
2010-01-06 45.40
2010-01-07 45.69
Freq: D, Name: HP, dtype: float64
b
What would be the output from the following code: import numpy as np arr1 = np.array(([1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16])) arr2 = arr1[1:3,1:3] / 2 print arr2 Select one answer: a) [[3 3.5][5 5.5]] b) [3 3.5 5 5.5] c) [[1 3][5 7]] d) [[3 3][5 5]]
d
Given the following dataframe, df: df = SPY 2016-01-01 10 2016-01-02 11 2016-01-03 NaN 2016-01-04 NaN 2016-01-05 14 2016-01-06 15 Which line of code would produce the following output: df = SPY 2016-01-01 10 2016-01-02 11 2016-01-03 14 2016-01-04 14 2016-01-05 14 2016-01-06 15 Options: a) df = df.fillna(method='ffill') b) df = df.fillna(method='bfill') c) df = df.dropna() d) df = df.interpolate()
b
What is the output of this python code? In this example, we provide Python code, and then several potential example answers. Code: import numpy as np test = np.array([1, 2, 3, 4, 5, 6]) test.shape = (2,3) print test.cumsum(axis = 0) Potential output below, select one answer: a) [5 , 7 , 9] b) [[ 1 , 3 , 6] [ 4 , 9 , 15]] c) [[1 , 2] [4 , 6] [9 , 12]] d) [[ 1 , 2 , 3] [ 5 , 7 , 9]]
d
Question 1: Given the following code, choose which is the correct answer. Code: import numpy as np a = np.array([[1, 2],[4, 5]]) indices = np.array([1, 0]) print a[indices] Select one answer: a)[[4 5] [1 2]] b) [[1 2] [4 5] c) [[2 1] [5 4]] d) [[1 4] [2 5]]
a
"""What is the output of the following code?""" import numpy as np a = np.array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12], [13, 14, 15, 16]]) print a[2,:] """ Select one answer: a) [ 2 6 10 14] b) [ 5 6 7 8 ] c) [ 9 10 11 12 ] d) [ 12 11 10 9 ]
c
Section 4
(45 cards)