# Lab hands-on Numpy and Pandas

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numpy.array
create an array of items optimised for data analysis.
import numpy as np

# Data loaded into a Python list structure
data = [50,50,47,97,49,3,53,42,26,74,82,62,37]

# Data is optimised for numeric analysis
val = np.array(data)

print (type(data),'x 2:', data * 2)
print (type(val),'x 2:', grades * 2)

Result:
<class 'list'> x 2: [50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37, 50, 50, 47, 97, 49, 3, 53, 42, 26, 74, 82, 62, 37]
<class 'numpy.ndarray'> x 2: [100 100 94 194 98 6 106 84 52 148 164 124 74]

numpy.shape
Return shape of an array

np.shape(val)
# or
val.shape

Result:
(13,)
13 elements (one dimension aray)

numpy.mean()
Return arithmetic mean (average)

import numpy as np

data = [50,50,47,97,49,3,53,42,26,74]
val = np.array(data)

val.mean()

Result:
49.1

Display numpay array.

import numpy as np

study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]

print(student_data)student_data

Result:
[[10. 11.5 9. 16. 9.25 1. 11.5 9. 8.5 14.5 15.5 13.75
9. 8. 15.5 8. 9. 6. 10. 12. 12.5 12. ]
[50. 50. 47. 97. 49. 3. 53. 42. 26. 74. 82. 62.
37. 15. 70. 27. 36. 35. 48. 52. 63. 64. ]]
array([[10. , 11.5 , 9. , 16. , 9.25, 1. , 11.5 , 9. , 8.5 ,
14.5 , 15.5 , 13.75, 9. , 8. , 15.5 , 8. , 9. , 6. ,
10. , 12. , 12.5 , 12. ],
[50. , 50. , 47. , 97. , 49. , 3. , 53. , 42. , 26. ,
74. , 82. , 62. , 37. , 15. , 70. , 27. , 36. , 35. ,
48. , 52. , 63. , 64. ]])

Format output of a float number
Set number of decimals to be shown after decimal point
import numpy as np

study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]

avg_study = student_data[0].mean()

# avg_study value will go into the first {:.2f} and the avg_grade value will go into the second
# {:.2f} mans value will be shown as float number with 2 fingers after decimal point and as many as necessary before decimal point.

Result:
Average study hours: 10.52

Multidimensional numpy array
import numpy as np

data = [50,50,47,97,49,3,53,42,26,74,82,62,37,15,70,27,36,35,48,52,63,64]

study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,
13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]

name = ['Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie',
'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny',
'Jakeem','Helena','Ismat','Anila','Skye','Daniel','Aisha']

student_data.shape

Result:
(3,22)
3 arrays, each with 22 elements.

Panda dataframe
Numpy manages perfect unidimensional data. Pandas is used to manipulate multidimensional data. It uses dataframes.

import pandas as pd

study_hours = [10.0,11.5,9.0,16.0,9.25,1.0,11.5,9.0,8.5,14.5,15.5,
13.75,9.0,8.0,15.5,8.0,9.0,6.0,10.0,12.0,12.5,12.0]
names = ['Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie',
'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny',
'Jakeem','Helena','Ismat','Anila','Skye','Daniel','Aisha']
# create dataframe
df_students = pd.DataFrame({'Names':names,
'StudyHours':study_hours,

# Display dataframe in tabular format
df_students

Result:

Finding data in a DataFrame

Find one record (all columns)
df_students.loc[5]

Result:
Names Vicky
StudyHours 1.0
Name: 5, dtype: object

Find one record, one colums

df_students.loc[0,'Names']

Result:
'Dan'

Find multiple records (all record: all columns)

# Show records from location/key 0 to 5 (inclusive)
df_students.loc[0:3]

# Show records from location/key 0 to 3 (exclude location 3)
df_students.iloc[0:3]

Find multiple records, two columns.

df_students.iloc[0:3,[1,2]]

Result:

010.050
111.550
29.047

Filter data

df_students[df_students['Names']=='Aisha']
# or
df_students[df_students['Name's]=='Aisha']
# or
df_students[df_students.Name == 'Aisha']
# or
df_students.query('Names=="Aisha"')

Result:
21Aisha12.064

import pandas as pd

# For windows, you must have wget installed
# Not? Download it from https://www.gnu.org/software/wget/ then add location where it is wget.exe to PATH environment variable. How to? Search on the net.

# Show the firts 5 records from dataframe

Result
0Dan10.0050.0
1Joann11.5050.0
2Pedro9.0047.0
3Rosie16.0097.0
4Ethan9.2549.0

Dataframe missing values.

df_students.isnull()
For every item in all records and all columns display FALSE if value is NOT NULL and TRUE is value is NULL

df_students.isnull().sum()
Show sum of NULL values per every colums

# Get all records that has NULL value on any columns.
# axis=1 means every row
df_students[df_students.isnull().any(axis=1)]

Result:
22Bill8.0NaN
23TedNaNNaN

Dealing with dataframe null values

# Replace with mean (columns values must be numeric)
df_students.StudyHours = df_students.StudyHours.fillna(df_students.StudyHours.mean())
df_students[df_students.isnull().any(axis=1)]

Result:

#
22Bill8.000000NaN
23Ted10.413043NaN

# Delete records that contains NULL values
# axis=0 means any columns
df_students = df_students.dropna(axis=0, how='any')
df_students[df_students.isnull().any(axis=1)]

Result:
Nothing shown because - not NULL values in dataframe.

Explore dataframe
# Get mean
mean_study = df_students['StudyHours'].mean()

# Get students who studied more than average (mean)
df_students[df_students.StudyHours > mean_study]
df_students

Result:

Name

#
1Joann11.5050.0
3Rosie16.0097.0
6Frederic11.5053.0
9Giovanni14.5074.0

Result:
66.7

#Assume pass grade is 60, shoe all students with pass TRUE or FALSE
passes

Result:
0 False
1 False
2 False
3 True
4 False
..
..

#Create a new column and add to dataframe ; axix=1 means add a column
df_students = pd.concat([df_students, paasses.rename("Pass")], axis=1);df_students
df_students

Result:

Name

#
0Dan10.0050.0False
1Joann11.5050.0False
2Pedro9.0047.0False
3Rosie16.0097.0True
4Ethan9.2549.0False

# Groupby
r = df_students.groupby(df_students.Pass).Name.count()
print(r)

Result:
Pass
False 15
True 7
Name: Name, dtype: int64

Sort and replace original dataframe with the result.