금융공학프로그래밍3, Quiz5

Problem 1

  1. Create a 2-dimensional ndarray object ‘Arr’ with the code below and answer the following.
import numpy as np
np.random.seed(123)
Arr=np.random.randn(8,10)
  1. Write an expression to calculate column sum of Arr.
np.sum(Arr,axis=0)
array([-3.68648899, -3.20797062,  1.89628963, -4.31697028, -1.68652947,
        3.31014718,  3.61439765,  2.82609217,  1.68894737,  0.84241575])
  1. Write an expression that finds the position (row and column index) of an element greater than 2 in Arr
np.where(Arr>2)
(array([1, 1, 4, 4]), array([6, 7, 6, 9]))

Problem 2

  1. Write 3 different expressions to create a pandas Series object ‘S’ displayed as follows.

    >>> S

    a 0

    b 1

    c 2

    d 3

    dtype: int64

import pandas as pd
S=pd.Series([0,1,2,3],index=['a','b','c','d'])
S
a    0
b    1
c    2
d    3
dtype: int64
S=pd.Series(dict(a=0,b=1,c=2,d=3))
S
a    0
b    1
c    2
d    3
dtype: int64
S=pd.Series(np.arange(4),index=['a','b','c','d'])
S
a    0
b    1
c    2
d    3
dtype: int64

Problem 3

  1. Write 4 different expressions to select the 2nd and the 3rd elements of Snew.
Snew = pd.Series({'a': 1, 'b':4, 'c':2, 'd':3} )
Snew[1:3]
b    4
c    2
dtype: int64
Snew.iloc[[1,2]]
b    4
c    2
dtype: int64
Snew['b':'c']
b    4
c    2
dtype: int64
Snew[['b','c']]
b    4
c    2
dtype: int64

Problem 4~5

np.random.seed(123)
DF = pd.DataFrame(np.random.randn(6,7),
                  columns=list('abcdefg'),
                  index=[3,2,4,5,1,0])
DF
a b c d e f g
3 -1.085631 0.997345 0.282978 -1.506295 -0.578600 1.651437 -2.426679
2 -0.428913 1.265936 -0.866740 -0.678886 -0.094709 1.491390 -0.638902
4 -0.443982 -0.434351 2.205930 2.186786 1.004054 0.386186 0.737369
5 1.490732 -0.935834 1.175829 -1.253881 -0.637752 0.907105 -1.428681
1 -0.140069 -0.861755 -0.255619 -2.798589 -1.771533 -0.699877 0.927462
0 -0.173636 0.002846 0.688223 -0.879536 0.283627 -0.805367 -1.727669
  1. Fill in the appropriate expression in square brackets so you can select columns ‘c’ and ‘e’.
DF.iloc[:,2:5:2]
c e
3 0.282978 -0.578600
2 -0.866740 -0.094709
4 2.205930 1.004054
5 1.175829 -0.637752
1 -0.255619 -1.771533
0 0.688223 0.283627 
DF[['c','e']]
c e
3 0.282978 -0.578600
2 -0.866740 -0.094709
4 2.205930 1.004054
5 1.175829 -0.637752
1 -0.255619 -1.771533
0 0.688223 0.283627
  1. Fill in the appropriate expression in square brackets so that you can select rows with negative values for column ‘c’.
DF.loc[DF.c<0,:]
a b c d e f g
2 -0.428913 1.265936 -0.866740 -0.678886 -0.094709 1.491390 -0.638902
1 -0.140069 -0.861755 -0.255619 -2.798589 -1.771533 -0.699877 0.927462 
DF[DF.c<0]
a b c d e f g
2 -0.428913 1.265936 -0.866740 -0.678886 -0.094709 1.491390 -0.638902
1 -0.140069 -0.861755 -0.255619 -2.798589 -1.771533 -0.699877 0.927462