Python coding exercise-numpy and pandas part 2

Q1:convert a numpy array to a dataframe of given shape¶

In [1]:

import numpy as np
import pandas as pd

In [17]:

arr1=np.random.rand(35)
arr1

Out[17]:

array([0.93396756, 0.86539523, 0.2219763 , 0.02386981, 0.52899722,
       0.48128182, 0.36097828, 0.32840324, 0.65400887, 0.75653592,
       0.47091215, 0.79927516, 0.71212087, 0.39202379, 0.18226057,
       0.27436685, 0.09453414, 0.26264447, 0.50901002, 0.76229193,
       0.76573485, 0.82551178, 0.16686845, 0.58544734, 0.55156004,
       0.72466356, 0.42176526, 0.4213822 , 0.99291738, 0.77693886,
       0.13278228, 0.73468001, 0.36669957, 0.5242279 , 0.69553251])

In [18]:

arr1.shape

Out[18]:

(35,)

So the array has 35 rows and 1 column.It could represent daily temperature readings for 5 weeks.Lets say we want to convert it into a data frame with 7 rows and 5 columns, where each column represents data collected for individual week and rows could represent days¶

In [22]:

df=pd.DataFrame(arr1)
df.columns=['Temperature_Reading']
df.head()

Out[22]:

	Temperature_Reading
0	0.933968
1	0.865395
2	0.221976
3	0.023870
4	0.528997

In [23]:

arr2=np.reshape(arr1,(7,5))
arr2

Out[23]:

array([[0.93396756, 0.86539523, 0.2219763 , 0.02386981, 0.52899722],
       [0.48128182, 0.36097828, 0.32840324, 0.65400887, 0.75653592],
       [0.47091215, 0.79927516, 0.71212087, 0.39202379, 0.18226057],
       [0.27436685, 0.09453414, 0.26264447, 0.50901002, 0.76229193],
       [0.76573485, 0.82551178, 0.16686845, 0.58544734, 0.55156004],
       [0.72466356, 0.42176526, 0.4213822 , 0.99291738, 0.77693886],
       [0.13278228, 0.73468001, 0.36669957, 0.5242279 , 0.69553251]])

In [26]:

df2=pd.DataFrame(arr2)
df2.columns=['Week1','Week2','Week3','Week4','Week4']
df2

Out[26]:

	Week1	Week2	Week3	Week4	Week4
0	0.933968	0.865395	0.221976	0.023870	0.528997
1	0.481282	0.360978	0.328403	0.654009	0.756536
2	0.470912	0.799275	0.712121	0.392024	0.182261
3	0.274367	0.094534	0.262644	0.509010	0.762292
4	0.765735	0.825512	0.166868	0.585447	0.551560
5	0.724664	0.421765	0.421382	0.992917	0.776939
6	0.132782	0.734680	0.366700	0.524228	0.695533

Important Note:Arrays with dimensions M by N can be reshaped into an array with dimensions K by L as long as MN = K L¶

Q2:Find the positions of numbers that are multiples of 3 from a series¶

In [51]:

s1=pd.Series(np.array([1,3,6,4,5,9,9]))
s1

Out[51]:

0    1
1    3
2    6
3    4
4    5
5    9
6    9
dtype: int32

In [54]:

l1=list(s1)
l1.index(9)

Out[54]:

5

We can see that ideally it should have been 5 and 6, but here we only get the index of the first instance of 9.Hence we will be using enumerate to execute this.Enumerate gives a list with index and values of list elements¶

In [55]:

list(enumerate(l1))

Out[55]:

[(0, 1), (1, 3), (2, 6), (3, 4), (4, 5), (5, 9), (6, 9)]

In [57]:

pos=[i for i,j in enumerate(l1) if j % 3 == 0]
pos

Out[57]:

[1, 2, 5, 6]

Q3:Extract items at given positions from a series¶

In [83]:

s1 = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))
s1[:6]

Out[83]:

0    a
1    b
2    c
3    d
4    e
5    f
dtype: object

In [84]:

# Use the below position and extract the corresponding elements from s1
pos = [0, 4, 8, 14, 20]

In [88]:

s1[s1.index.isin(pos)]

Out[88]:

0     a
4     e
8     i
14    o
20    u
dtype: object

Q4: Stack two series vertically and horizontally¶

In [89]:

s1=pd.Series(range(5))
s1

Out[89]:

0    0
1    1
2    2
3    3
4    4
dtype: int64

In [90]:

s2=pd.Series(list('abcde'))
s2

Out[90]:

0    a
1    b
2    c
3    d
4    e
dtype: object

Stacking vertically¶

In [94]:

pd.concat([s1,s2])

Out[94]:

0    0
1    1
2    2
3    3
4    4
0    a
1    b
2    c
3    d
4    e
dtype: object

Stacking horizontally¶

In [96]:

pd.concat([s1,s2],axis=1)

Out[96]:

	0	1
0	0	a
1	1	b
2	2	c
3	3	d
4	4	e

Get the positions of items of series A in another series B¶

In [99]:

sA = pd.Series([10, 9, 6, 5, 3, 1, 12, 8, 13])
sA

Out[99]:

0    10
1     9
2     6
3     5
4     3
5     1
6    12
7     8
8    13
dtype: int64

In [100]:

sB = pd.Series([1, 3, 10, 13])
sB

Out[100]:

0     1
1     3
2    10
3    13
dtype: int64

In [102]:

pos_logical=sA.isin(sB)
pos_logical

Out[102]:

0     True
1    False
2    False
3    False
4     True
5     True
6    False
7    False
8     True
dtype: bool

In [105]:

pos=list(sA.index[pos_logical])
pos

Out[105]:

[0, 4, 5, 8]