In [1]:
emp_master= {"Emp_Name": ["Adam", "Mark", "Deb", "Antony", "Joseph"],
       "Title": ["Manager", "Team Lead", "Member", "Member", "Member"],
       "Attendence%": [70, 82, 70, 95, 99],
       "BMI": [35, 34, 16.5, 20, 22] }
In [2]:
import numpy as np
import pandas as pd
In [3]:
empmaster_table = pd.DataFrame(emp_master)
print(empmaster_table)
  Emp_Name      Title  Attendence%   BMI
0     Adam    Manager           70  35.0
1     Mark  Team Lead           82  34.0
2      Deb     Member           70  16.5
3   Antony     Member           95  20.0
4   Joseph     Member           99  22.0
In [4]:
empmaster_table.index=["E001", "E028", "E392", "E009", "E983"]
print(empmaster_table)
     Emp_Name      Title  Attendence%   BMI
E001     Adam    Manager           70  35.0
E028     Mark  Team Lead           82  34.0
E392      Deb     Member           70  16.5
E009   Antony     Member           95  20.0
E983   Joseph     Member           99  22.0
In [5]:
empmaster_table.index.name='Emp No'
In [6]:
print(empmaster_table)
       Emp_Name      Title  Attendence%   BMI
Emp No                                       
E001       Adam    Manager           70  35.0
E028       Mark  Team Lead           82  34.0
E392        Deb     Member           70  16.5
E009     Antony     Member           95  20.0
E983     Joseph     Member           99  22.0
In [7]:
DS=pd.read_excel("F:/2019 GB Python/GB Pandas Data Frame.xls")
In [8]:
DS.head()
Out[8]:
S.No. Unnamed: 1 PurType PayVal WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
0 1 NaN A 155.0 9 2.0 5.5 6.5 5.0 25 Monday
1 2 NaN B 175.0 13 1.2 5.6 9.5 6.5 55 Wednesday
2 3 NaN C 98.0 10 2.3 6.3 6.3 6.0 40 Friday
3 4 NaN D 125.0 8 2.0 4.9 5.4 5.5 35 Monday
4 5 NaN B 450.0 15 1.5 7.2 10 8.0 65 Monday
In [9]:
DS=pd.read_excel("F:/2019 GB Python/GB Pandas Data Frame.xls", index_col=0)
print(DS.index.name)
S.No.
In [10]:
DS.shape
Out[10]:
(90, 10)
In [11]:
DS.dtypes
Out[11]:
Unnamed: 1            float64
PurType                object
PayVal                float64
WaitList                int64
Process               float64
WaitTime              float64
ApproveTime            object
Disbursement Time     float64
 Overall                int64
Day of week            object
dtype: object
In [12]:
DS.isna().any()
Out[12]:
Unnamed: 1             True
PurType               False
PayVal                 True
WaitList              False
Process               False
WaitTime              False
ApproveTime           False
Disbursement Time     False
 Overall              False
Day of week           False
dtype: bool
In [14]:
DS1=DS.dropna(axis=1)
DS1.head()
Out[14]:
PurType WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
S.No.
1 A 9 2.0 5.5 6.5 5.0 25 Monday
2 B 13 1.2 5.6 9.5 6.5 55 Wednesday
3 C 10 2.3 6.3 6.3 6.0 40 Friday
4 D 8 2.0 4.9 5.4 5.5 35 Monday
5 B 15 1.5 7.2 10 8.0 65 Monday
In [16]:
DS1.shape
Out[16]:
(90, 8)
In [17]:
DS.dropna(axis=1, inplace=True)
In [18]:
DS
Out[18]:
PurType WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
S.No.
1 A 9 2.0 5.5 6.5 5.0 25 Monday
2 B 13 1.2 5.6 9.5 6.5 55 Wednesday
3 C 10 2.3 6.3 6.3 6.0 40 Friday
4 D 8 2.0 4.9 5.4 5.5 35 Monday
5 B 15 1.5 7.2 10 8.0 65 Monday
6 B 18 2.4 8.5 12 8.0 75 Tuesday
7 A 9 2.5 6.0 7 4.5 22 Monday
8 A 8 4.0 4.6 5.6 4.0 35 Tuesday
9 B 20 3.0 11.0 14 8.0 88 Wednesday
10 C 10 4.0 5.6 5.6 6.7 52 Monday
11 D 7 2.4 3.5 4 6.5 26 Tuesday
12 A 6 3.5 3.5 4.5 5.0 28 Monday
13 B 12 4.2 7.0 10 8.0 56 Wednesday
14 A 5 3.0 4.1 5 5.4 37 Monday
15 D 10 4.2 6.0 6.5 5.6 38 Wednesday
16 D 14 1.3 7.2 8 7.3 45 Monday
17 B 9 1.9 6.8 9.5 6.5 54 Wednesday
18 B 14 1.3 5.4 8.3 8.0 49 Friday
19 D 14 1.6 7.0 7.6 8.0 47 Friday
20 B 9 1.3 5.6 8.3 6.0 35 Monday
21 B 15 2.1 6.0 9.8 8.3 55 Tuesday
22 A 7 1.8 4.3 5 6.0 0 Wednesday
23 C 16 2.1 7.8 7.8 7.0 48 Wednesday
24 C 3 2.0 5.6 5.6 6.5 37 Wednesday
25 D 13 2.3 7.5 8 8.5 45 Monday
26 A 9 2.5 6.0 6.8 5.0 34 Thursday
27 C 11 2.6 7.4 7.4 4.5 43 Monday
28 B 17 1.2 9.5 14.5 8.0 85 Tuesday
29 C 9 2.3 8.0 8 9.0 38 Wednesday
30 B 13 3.4 6.8 9.5 6.0 53 Friday
... ... ... ... ... ... ... ... ...
61 C 13 2.0 8.0 8 8.0 47 Wednesday
62 D 12 2.3 6.5 7 9.0 48 Wednesday
63 D 14 2.3 11.0 12 8.0 54 Friday
64 A 8 2.0 5.0 5 6.5 29 Friday
65 C 8 1.7 6.5 6.5 7.8 33 Monday
66 D 12 1.5 8.0 8.5 7.0 46 Tuesday
67 D 10 3.0 5.0 5.6 7.0 46 Tuesday
68 A 6 2.5 4.8 5.6 5.0 27 Tuesday
69 D 11 2.4 10.5 11.2 8.0 56 Tuesday
70 D 13 1.8 8.0 9 7.0 48 Wednesday
71 A 7 1.9 5.4 5.4 6.0 23 Friday
72 B 9 2.0 5.5 7 9.0 34 Monday
73 A 11 2.3 4.0 4 5.0 23 Monday
74 B 10 2.2 3.0 4 6.5 24 Monday
75 D 8 1.7 6.0 6 6.5 29 Wednesday
76 C 12 2.2 5.0 5 5.0 22 Thursday
77 B 6 2.1 8.5 11 8.0 58 Wednesday
78 D 10 1.8 6.0 6.5 6.0 33 Wednesday
79 B 9 1.5 5.5 7 7.0 34 Monday
80 C 10 2.4 6.5 6.5 7.0 31 Monday
81 A 8 2.1 6.0 6.5 5.0 23 Monday
82 D 6 1.9 3.0 3 4.0 18 Monday
83 D 8 2.0 5.4 5.4 8.0 31 Friday
84 A 7 5.4 6.4 6.4 7.0 28 Wednesday
85 D 14 3.2 6.5 6.5 9.0 51 Tuesday
86 B 6 2.3 8.5 11 8.0 51 Tuesday
87 C 5 2.0 3.5 3.5 3.0 18 Monday
88 A 14 3.0 4.2 4.8 5.0 25 Monday
89 D 10 2.0 7.5 8 8.0 57 Tuesday
90 B 5 2.0 6.5 8 9.0 38 Thursday

90 rows × 8 columns

In [19]:
DS=pd.read_excel("F:/2019 GB Python/GB Pandas Data Frame.xls", index_col=0)
In [20]:
DS1=DS.drop("Unnamed: 1", axis=1)
DS1
Out[20]:
PurType PayVal WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
S.No.
1 A 155.0 9 2.0 5.5 6.5 5.0 25 Monday
2 B 175.0 13 1.2 5.6 9.5 6.5 55 Wednesday
3 C 98.0 10 2.3 6.3 6.3 6.0 40 Friday
4 D 125.0 8 2.0 4.9 5.4 5.5 35 Monday
5 B 450.0 15 1.5 7.2 10 8.0 65 Monday
6 B 350.0 18 2.4 8.5 12 8.0 75 Tuesday
7 A 176.0 9 2.5 6.0 7 4.5 22 Monday
8 A 154.0 8 4.0 4.6 5.6 4.0 35 Tuesday
9 B 134.0 20 3.0 11.0 14 8.0 88 Wednesday
10 C 235.0 10 4.0 5.6 5.6 6.7 52 Monday
11 D 245.0 7 2.4 3.5 4 6.5 26 Tuesday
12 A 123.0 6 3.5 3.5 4.5 5.0 28 Monday
13 B NaN 12 4.2 7.0 10 8.0 56 Wednesday
14 A 180.0 5 3.0 4.1 5 5.4 37 Monday
15 D 210.0 10 4.2 6.0 6.5 5.6 38 Wednesday
16 D 340.0 14 1.3 7.2 8 7.3 45 Monday
17 B 542.0 9 1.9 6.8 9.5 6.5 54 Wednesday
18 B 143.0 14 1.3 5.4 8.3 8.0 49 Friday
19 D 176.0 14 1.6 7.0 7.6 8.0 47 Friday
20 B 154.0 9 1.3 5.6 8.3 6.0 35 Monday
21 B 234.0 15 2.1 6.0 9.8 8.3 55 Tuesday
22 A 159.0 7 1.8 4.3 5 6.0 0 Wednesday
23 C 245.0 16 2.1 7.8 7.8 7.0 48 Wednesday
24 C 345.0 3 2.0 5.6 5.6 6.5 37 Wednesday
25 D 450.0 13 2.3 7.5 8 8.5 45 Monday
26 A 187.0 9 2.5 6.0 6.8 5.0 34 Thursday
27 C 146.0 11 2.6 7.4 7.4 4.5 43 Monday
28 B NaN 17 1.2 9.5 14.5 8.0 85 Tuesday
29 C 453.0 9 2.3 8.0 8 9.0 38 Wednesday
30 B 245.0 13 3.4 6.8 9.5 6.0 53 Friday
... ... ... ... ... ... ... ... ... ...
61 C 543.0 13 2.0 8.0 8 8.0 47 Wednesday
62 D 123.0 12 2.3 6.5 7 9.0 48 Wednesday
63 D 100.0 14 2.3 11.0 12 8.0 54 Friday
64 A 150.0 8 2.0 5.0 5 6.5 29 Friday
65 C 123.0 8 1.7 6.5 6.5 7.8 33 Monday
66 D 246.0 12 1.5 8.0 8.5 7.0 46 Tuesday
67 D 234.0 10 3.0 5.0 5.6 7.0 46 Tuesday
68 A 120.0 6 2.5 4.8 5.6 5.0 27 Tuesday
69 D 187.0 11 2.4 10.5 11.2 8.0 56 Tuesday
70 D 146.0 13 1.8 8.0 9 7.0 48 Wednesday
71 A 203.0 7 1.9 5.4 5.4 6.0 23 Friday
72 B 210.0 9 2.0 5.5 7 9.0 34 Monday
73 A 230.0 11 2.3 4.0 4 5.0 23 Monday
74 B 330.0 10 2.2 3.0 4 6.5 24 Monday
75 D 230.0 8 1.7 6.0 6 6.5 29 Wednesday
76 C 340.0 12 2.2 5.0 5 5.0 22 Thursday
77 B 432.0 6 2.1 8.5 11 8.0 58 Wednesday
78 D 134.0 10 1.8 6.0 6.5 6.0 33 Wednesday
79 B 165.0 9 1.5 5.5 7 7.0 34 Monday
80 C 176.0 10 2.4 6.5 6.5 7.0 31 Monday
81 A 231.0 8 2.1 6.0 6.5 5.0 23 Monday
82 D 320.0 6 1.9 3.0 3 4.0 18 Monday
83 D 156.0 8 2.0 5.4 5.4 8.0 31 Friday
84 A 546.0 7 5.4 6.4 6.4 7.0 28 Wednesday
85 D 324.0 14 3.2 6.5 6.5 9.0 51 Tuesday
86 B 234.0 6 2.3 8.5 11 8.0 51 Tuesday
87 C 121.0 5 2.0 3.5 3.5 3.0 18 Monday
88 A 187.0 14 3.0 4.2 4.8 5.0 25 Monday
89 D 120.0 10 2.0 7.5 8 8.0 57 Tuesday
90 B 140.0 5 2.0 6.5 8 9.0 38 Thursday

90 rows × 9 columns

In [21]:
DS1.loc[(DS1==0).any(axis=1)]
Out[21]:
PurType PayVal WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
S.No.
22 A 159.0 7 1.8 4.3 5 6.0 0 Wednesday
33 C 98.0 14 1.5 6.3 6.3 6.0 0 Tuesday
35 A 231.0 9 1.7 5.0 5.6 6.9 0 Monday
In [23]:
DS2=DS1[DS1[' Overall'] !=0]
DS2
Out[23]:
PurType PayVal WaitList Process WaitTime ApproveTime Disbursement Time Overall Day of week
S.No.
1 A 155.0 9 2.0 5.5 6.5 5.0 25 Monday
2 B 175.0 13 1.2 5.6 9.5 6.5 55 Wednesday
3 C 98.0 10 2.3 6.3 6.3 6.0 40 Friday
4 D 125.0 8 2.0 4.9 5.4 5.5 35 Monday
5 B 450.0 15 1.5 7.2 10 8.0 65 Monday
6 B 350.0 18 2.4 8.5 12 8.0 75 Tuesday
7 A 176.0 9 2.5 6.0 7 4.5 22 Monday
8 A 154.0 8 4.0 4.6 5.6 4.0 35 Tuesday
9 B 134.0 20 3.0 11.0 14 8.0 88 Wednesday
10 C 235.0 10 4.0 5.6 5.6 6.7 52 Monday
11 D 245.0 7 2.4 3.5 4 6.5 26 Tuesday
12 A 123.0 6 3.5 3.5 4.5 5.0 28 Monday
13 B NaN 12 4.2 7.0 10 8.0 56 Wednesday
14 A 180.0 5 3.0 4.1 5 5.4 37 Monday
15 D 210.0 10 4.2 6.0 6.5 5.6 38 Wednesday
16 D 340.0 14 1.3 7.2 8 7.3 45 Monday
17 B 542.0 9 1.9 6.8 9.5 6.5 54 Wednesday
18 B 143.0 14 1.3 5.4 8.3 8.0 49 Friday
19 D 176.0 14 1.6 7.0 7.6 8.0 47 Friday
20 B 154.0 9 1.3 5.6 8.3 6.0 35 Monday
21 B 234.0 15 2.1 6.0 9.8 8.3 55 Tuesday
23 C 245.0 16 2.1 7.8 7.8 7.0 48 Wednesday
24 C 345.0 3 2.0 5.6 5.6 6.5 37 Wednesday
25 D 450.0 13 2.3 7.5 8 8.5 45 Monday
26 A 187.0 9 2.5 6.0 6.8 5.0 34 Thursday
27 C 146.0 11 2.6 7.4 7.4 4.5 43 Monday
28 B NaN 17 1.2 9.5 14.5 8.0 85 Tuesday
29 C 453.0 9 2.3 8.0 8 9.0 38 Wednesday
30 B 245.0 13 3.4 6.8 9.5 6.0 53 Friday
31 D 121.0 12 2.0 6.0 - 7.0 50 Monday
... ... ... ... ... ... ... ... ... ...
61 C 543.0 13 2.0 8.0 8 8.0 47 Wednesday
62 D 123.0 12 2.3 6.5 7 9.0 48 Wednesday
63 D 100.0 14 2.3 11.0 12 8.0 54 Friday
64 A 150.0 8 2.0 5.0 5 6.5 29 Friday
65 C 123.0 8 1.7 6.5 6.5 7.8 33 Monday
66 D 246.0 12 1.5 8.0 8.5 7.0 46 Tuesday
67 D 234.0 10 3.0 5.0 5.6 7.0 46 Tuesday
68 A 120.0 6 2.5 4.8 5.6 5.0 27 Tuesday
69 D 187.0 11 2.4 10.5 11.2 8.0 56 Tuesday
70 D 146.0 13 1.8 8.0 9 7.0 48 Wednesday
71 A 203.0 7 1.9 5.4 5.4 6.0 23 Friday
72 B 210.0 9 2.0 5.5 7 9.0 34 Monday
73 A 230.0 11 2.3 4.0 4 5.0 23 Monday
74 B 330.0 10 2.2 3.0 4 6.5 24 Monday
75 D 230.0 8 1.7 6.0 6 6.5 29 Wednesday
76 C 340.0 12 2.2 5.0 5 5.0 22 Thursday
77 B 432.0 6 2.1 8.5 11 8.0 58 Wednesday
78 D 134.0 10 1.8 6.0 6.5 6.0 33 Wednesday
79 B 165.0 9 1.5 5.5 7 7.0 34 Monday
80 C 176.0 10 2.4 6.5 6.5 7.0 31 Monday
81 A 231.0 8 2.1 6.0 6.5 5.0 23 Monday
82 D 320.0 6 1.9 3.0 3 4.0 18 Monday
83 D 156.0 8 2.0 5.4 5.4 8.0 31 Friday
84 A 546.0 7 5.4 6.4 6.4 7.0 28 Wednesday
85 D 324.0 14 3.2 6.5 6.5 9.0 51 Tuesday
86 B 234.0 6 2.3 8.5 11 8.0 51 Tuesday
87 C 121.0 5 2.0 3.5 3.5 3.0 18 Monday
88 A 187.0 14 3.0 4.2 4.8 5.0 25 Monday
89 D 120.0 10 2.0 7.5 8 8.0 57 Tuesday
90 B 140.0 5 2.0 6.5 8 9.0 38 Thursday

87 rows × 9 columns

In [ ]:
DS2=DS1[DS1.Overall !=0]