Tuesday, 30 June 2020

Python : Pandas - 3



# Rename a column
cars.rename(columns = {'wt':'weight'})
cars
Out[19]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
0
Mazda RX4
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
Mazda RX4 Wag
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
Datsun 710
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
3
Hornet 4 Drive
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
4
Hornet Sportabout
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
5
Valiant
18.1
6
225.0
105
2.76
3.460
20.22
1
0
3
1
6
Duster 360
14.3
8
360.0
245
3.21
3.570
15.84
0
0
3
4
7
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
9
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
10
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
11
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
3
12
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
3
13
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
3
14
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
4
15
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
4
16
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
4
17
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
20
Toyota Corona
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
1
21
Dodge Challenger
15.5
8
318.0
150
2.76
3.520
16.87
0
0
3
2
22
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
2
23
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
4
24
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
2
25
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
26
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
27
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
28
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
29
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
30
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
31
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
. . .

cars1 = cars.drop(columns=['carb'])
cars1
Out[20]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
0
Mazda RX4
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
1
Mazda RX4 Wag
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
2
Datsun 710
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
3
Hornet 4 Drive
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
4
Hornet Sportabout
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
5
Valiant
18.1
6
225.0
105
2.76
3.460
20.22
1
0
3
6
Duster 360
14.3
8
360.0
245
3.21
3.570
15.84
0
0
3
7
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
8
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
9
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
10
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
11
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
12
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
13
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
14
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
15
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
16
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
17
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
18
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
19
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
20
Toyota Corona
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
21
Dodge Challenger
15.5
8
318.0
150
2.76
3.520
16.87
0
0
3
22
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
23
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
24
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
25
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
26
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
27
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
28
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
29
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
30
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
31
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
. . .

# corelation matrix
cars[['mpg','cyl','disp','hp','wt','gear','carb']].corr()
Out[21]:
mpg
cyl
disp
hp
wt
gear
carb
mpg
1.000000
-0.852162
-0.847551
-0.776168
-0.867659
0.480285
-0.550925
cyl
-0.852162
1.000000
0.902033
0.832447
0.782496
-0.492687
0.526988
disp
-0.847551
0.902033
1.000000
0.790949
0.887980
-0.555569
0.394977
hp
-0.776168
0.832447
0.790949
1.000000
0.658748
-0.125704
0.749812
wt
-0.867659
0.782496
0.887980
0.658748
1.000000
-0.583287
0.427606
gear
0.480285
-0.492687
-0.555569
-0.125704
-0.583287
1.000000
0.274073
carb
-0.550925
0.526988
0.394977
0.749812
0.427606
0.274073
1.000000








. . .
 cars
Out[22]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
0
Mazda RX4
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
Mazda RX4 Wag
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
Datsun 710
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
3
Hornet 4 Drive
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
4
Hornet Sportabout
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
5
Valiant
18.1
6
225.0
105
2.76
3.460
20.22
1
0
3
1
6
Duster 360
14.3
8
360.0
245
3.21
3.570
15.84
0
0
3
4
7
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
9
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
10
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
11
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
3
12
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
3
13
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
3
14
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
4
15
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
4
16
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
4
17
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
20
Toyota Corona
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
1
21
Dodge Challenger
15.5
8
318.0
150
2.76
3.520
16.87
0
0
3
2
22
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
2
23
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
4
24
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
2
25
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
26
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
27
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
28
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
29
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
30
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
31
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
. . .

# Manipulation
# first 4 rows
# iloc - Index Location
cars.iloc[:4]
Out[23]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
0
Mazda RX4
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
Mazda RX4 Wag
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
Datsun 710
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
3
Hornet 4 Drive
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
. . .

# from 5th row onwards
cars.iloc[4:]
Out[24]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
4
Hornet Sportabout
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
5
Valiant
18.1
6
225.0
105
2.76
3.460
20.22
1
0
3
1
6
Duster 360
14.3
8
360.0
245
3.21
3.570
15.84
0
0
3
4
7
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
9
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
10
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
11
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
3
12
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
3
13
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
3
14
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
4
15
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
4
16
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
4
17
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
20
Toyota Corona
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
1
21
Dodge Challenger
15.5
8
318.0
150
2.76
3.520
16.87
0
0
3
2
22
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
2
23
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
4
24
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
2
25
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
26
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
27
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
28
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
29
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
30
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
31
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
. . .



# only "hp" column from the cars dataframe
cars.iloc[:,4]
Out[25]:
0     110
1     110
2      93
3     110
4     175
5     105
6     245
7      62
8      95
9     123
10    123
11    180
12    180
13    180
14    205
15    215
16    230
17     66
18     52
19     65
20     97
21    150
22    150
23    245
24    175
25     66
26     91
27    113
28    264
29    175
30    335
31    109
Name: hp, dtype: int64
. . .

# first 5 rows of "hp" column from the cars dataframe
cars.iloc[0:5,4]
Out[26]:
0    110
1    110
2     93
3    110
4    175
Name: hp, dtype: int64
. . .

# AFTER first 5 rows of "hp" column from the cars dataframe
cars.iloc[5:,4]
Out[27]:
5     105
6     245
7      62
8      95
9     123
10    123
11    180
12    180
13    180
14    205
15    215
16    230
17     66
18     52
19     65
20     97
21    150
22    150
23    245
24    175
25     66
26     91
27    113
28    264
29    175
30    335
31    109
Name: hp, dtype: int64
. . .

# 5th row to 10th row for "hp" column
cars.iloc[5:10,4]
Out[28]:
5    105
6    245
7     62
8     95
9    123
Name: hp, dtype: int64
. . .

# 5th row to 10th row for 5th - 10th columns
# iloc[row_begin:row_end, column_begin:column_end]  # syntax
cars.iloc[5:10,4:10]
Out[29]:
hp
drat
wt
qsec
vs
am
5
105
2.76
3.46
20.22
1
0
6
245
3.21
3.57
15.84
0
0
7
62
3.69
3.19
20.00
1
0
8
95
3.92
3.15
22.90
1
0
9
123
3.92
3.44
18.30
1
0
. . .

cars.iloc[5:10:2,4:10] 
Out[30]:
hp
drat
wt
qsec
vs
am
5
105
2.76
3.46
20.22
1
0
7
62
3.69
3.19
20.00
1
0
9
123
3.92
3.44
18.30
1
0
. . .

cars.iloc[5:10:2,4:10:3] # # iloc[row_begin:row_end:interval, column_begin:column_end:interval]  Syntax
Out[31]:
hp
qsec
5
105
20.22
7
62
20.00
9
123
18.30
. . .
 # All rows & all columns of dataframe
cars.iloc[:,:]
Out[32]:
Unnamed: 0
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
0
Mazda RX4
21.0
6
160.0
110
3.90
2.620
16.46
0
1
4
4
1
Mazda RX4 Wag
21.0
6
160.0
110
3.90
2.875
17.02
0
1
4
4
2
Datsun 710
22.8
4
108.0
93
3.85
2.320
18.61
1
1
4
1
3
Hornet 4 Drive
21.4
6
258.0
110
3.08
3.215
19.44
1
0
3
1
4
Hornet Sportabout
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
5
Valiant
18.1
6
225.0
105
2.76
3.460
20.22
1
0
3
1
6
Duster 360
14.3
8
360.0
245
3.21
3.570
15.84
0
0
3
4
7
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
8
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
9
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
10
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
11
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
3
12
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
3
13
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
3
14
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
4
15
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
4
16
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
4
17
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
18
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
19
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
20
Toyota Corona
21.5
4
120.1
97
3.70
2.465
20.01
1
0
3
1
21
Dodge Challenger
15.5
8
318.0
150
2.76
3.520
16.87
0
0
3
2
22
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
2
23
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
4
24
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
2
25
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
26
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
27
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
28
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
29
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
30
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
31
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2


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