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Python import data, indexing, slicing

Python import data, indexing, slicing
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Common Shortcut

Select cell and press: Ctrl-Enter for run selected cells

  • Alt-Enter for run cell and insert below
  • A for insert new cell above selected cell
  • B for insert new cell below selected cell
  • M for make selected cell as markdown

Install Packages

pip install package_name
conda install package_name
use pip in command prompt
conda in Anaconda prompt

Working Directory

python
%pwd #pwd- Print Working Directory, will give you current working directory
text
'C:\\Users\\faisal\\Desktop\\Python\\Lesson-1'

Change directory

python
%cd C:\Users\faisal\Desktop\Python\Lesson-1 #cd- change directory
text
[WinError 2] The system cannot find the file specified: 'C:\\Users\\faisa\\Desktop\\Python\\Lesson-1 #cd- change directory'
C:\Users\faisa\Desktop\Python\Lesson-1

Load Packages

python
import numpy as np
import pandas as pd
import pyodbc #require for sql server connection

Import csv from Local Machine

python
df=pd.read_csv('cost_of_living.csv') #press shift Tab to check all available parameter
#df=pd.read_csv('c:\\Users\\faisa\\Desktop\\Python\\Lesson-1\\cost_of_living.csv',header=none)
#df=pd.read_csv(r'c:\Users\faisa\Desktop\Python\Lesson-1\cost_of_living.csv')
python
df.head()
text
Rank City Cost of Living Index Rent Index \
0 1 Hamilton, Bermuda 145.43 110.87
1 2 Zurich, Switzerland 141.25 66.14
2 3 Geneva, Switzerland 134.83 71.70
3 4 Basel, Switzerland 130.68 49.68
4 5 Bern, Switzerland 128.03 43.57
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
0 128.76 143.47 158.75
1 105.03 149.86 135.76
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
Local Purchasing Power Index
0 112.26
1 142.70
2 130.96
3 139.01
4 112.71

Output CSV

python
df.to_csv('mydf.csv',index=False) #Don't forget to add '.csv' at the end.
#df.to_csv(r'c:\Users\faisa\Desktop\Python\Lesson-1\my_df.csv',header=True,index=False) #Don't forget to add '.csv' at the end.
#df.to_csv ('C:\\Users\\faisa\\Desktop\\Python\\Lesson-1\\my_df.csv', header=True,index=False) #Don't forget to add '.csv' at the end.

Import xlsx from Local Machine

python
df_exl=pd.read_excel('cost_of_living_xl.xlsx', sheet_name='sheet1') #specify sheet name from your excel file

Output Excel

python
df_exl.to_excel('mydf.xlsx',sheet_name='Sheet1')

Import from SQL Server

python
cnxn = pyodbc.connect("Driver={SQL Server};"
"Server=DESKTOP-H3MCNFQ;"
"Database=mydb;")
# "uid=User;pwd=password")
df_sql = pd.read_sql_query('select * from [cost_of_living_2018]', cnxn)
python
df_sql.head()
text
Rank City Cost_of_Living_Index Rent_Index \
0 1 Hamilton, Bermuda 145.43 110.87
1 2 Zurich, Switzerland 141.25 66.14
2 3 Geneva, Switzerland 134.83 71.70
3 4 Basel, Switzerland 130.68 49.68
4 5 Bern, Switzerland 128.03 43.57
Cost_of_Living_Plus_Rent_Index Groceries_Index Restaurant_Price_Index \
0 128.76 143.47 158.75
1 105.03 149.86 135.76
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
Local_Purchasing_Power_Index
0 112.26
1 142.70
2 130.96
3 139.01
4 112.71

Import html Table

may need to install htmllib5,lxml, and BeautifulSoup4 packages:

conda install lxml
conda install html5lib
conda install BeautifulSoup4

python
df_html=pd.read_html('https://www.contextures.com/xlSampleData01.html',header=0)
python
df_html[0].head()
text
OrderDate Region Rep Item Units UnitCost Total
0 1/6/2018 East Jones Pencil 95 1.99 189.05
1 1/23/2018 Central Kivell Binder 50 19.99 999.50
2 2/9/2018 Central Jardine Pencil 36 4.99 179.64
3 2/26/2018 Central Gill Pen 27 19.99 539.73
4 3/15/2018 West Sorvino Pencil 56 2.99 167.44

Import Remote Data

python
df_git = pd.read_csv('https://raw.githubusercontent.com/cs109/2014_data/master/mtcars.csv')
df_git.head()
text
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
carb
0 4
1 4
2 1
3 1
4 2
python
df_git.to_csv ('C:\\Users\\faisa\\Desktop\\Python\\Lesson-1\\my_df_git.csv', header=True) #Don't forget to add '.csv' at the end.

Basic Information

python
df.shape
text
(538, 8)
python
df.columns
text
Index(['Rank', 'City', 'Cost of Living Index', 'Rent Index',
'Cost of Living Plus Rent Index', 'Groceries Index',
'Restaurant Price Index', 'Local Purchasing Power Index'],
dtype='object')
python
df.info()
text
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 538 entries, 0 to 537
Data columns (total 8 columns):
Rank 538 non-null int64
City 538 non-null object
Cost of Living Index 538 non-null float64
Rent Index 538 non-null float64
Cost of Living Plus Rent Index 538 non-null float64
Groceries Index 538 non-null float64
Restaurant Price Index 538 non-null float64
Local Purchasing Power Index 538 non-null float64
dtypes: float64(6), int64(1), object(1)
memory usage: 33.7+ KB
python
df.count()
text
Rank 538
City 538
Cost of Living Index 538
Rent Index 538
Cost of Living Plus Rent Index 538
Groceries Index 538
Restaurant Price Index 538
Local Purchasing Power Index 538
dtype: int64
python
df.sum()
text
Rank 144991
City Hamilton, BermudaZurich, SwitzerlandGeneva, Sw...
Cost of Living Index 34220
Rent Index 14624.4
Cost of Living Plus Rent Index 24769.5
Groceries Index 32062.1
Restaurant Price Index 31733.7
Local Purchasing Power Index 48515.9
dtype: object
python
df.min()
text
Rank 1
City Aachen, Germany
Cost of Living Index 20.86
Rent Index 3.82
Cost of Living Plus Rent Index 13.26
Groceries Index 19.26
Restaurant Price Index 12.06
Local Purchasing Power Index 1.88
dtype: object
python
df.max()
text
Rank 538
City Zurich, Switzerland
Cost of Living Index 145.43
Rent Index 115.36
Cost of Living Plus Rent Index 128.76
Groceries Index 149.86
Restaurant Price Index 158.75
Local Purchasing Power Index 168.93
dtype: object
python
df.describe()
text
Rank Cost of Living Index Rent Index \
count 538.000000 538.000000 538.000000
mean 269.500000 63.605874 27.182937
std 155.451493 21.359530 17.207302
min 1.000000 20.860000 3.820000
25% 135.250000 46.060000 13.002500
50% 269.500000 67.805000 25.095000
75% 403.750000 78.430000 35.432500
max 538.000000 145.430000 115.360000
Cost of Living Plus Rent Index Groceries Index \
count 538.000000 538.000000
mean 46.039944 59.594926
std 18.330342 22.168789
min 13.260000 19.260000
25% 30.997500 40.477500
50% 48.030000 61.630000
75% 58.005000 74.362500
max 128.760000 149.860000
Restaurant Price Index Local Purchasing Power Index
count 538.000000 538.000000
mean 58.984498 90.178271
std 26.243787 36.637241
min 12.060000 1.880000
25% 34.490000 58.087500
50% 64.065000 95.160000
75% 77.165000 120.140000
max 158.750000 168.930000
python
df.mean()
text
Rank 269.500000
Cost of Living Index 63.605874
Rent Index 27.182937
Cost of Living Plus Rent Index 46.039944
Groceries Index 59.594926
Restaurant Price Index 58.984498
Local Purchasing Power Index 90.178271
dtype: float64
python
df.median()
text
Rank 269.500
Cost of Living Index 67.805
Rent Index 25.095
Cost of Living Plus Rent Index 48.030
Groceries Index 61.630
Restaurant Price Index 64.065
Local Purchasing Power Index 95.160
dtype: float64
python
#df.isna() #will return True or False for each value, if null then True, if not null then False
df.isna().sum() #will return total number of null for each column
text
Rank 0
City 0
Cost of Living Index 0
Rent Index 0
Cost of Living Plus Rent Index 0
Groceries Index 0
Restaurant Price Index 0
Local Purchasing Power Index 0
dtype: int64

Basic Indexing and Selecting and Slicing

python
df['City']
text
0 Hamilton, Bermuda
1 Zurich, Switzerland
2 Geneva, Switzerland
3 Basel, Switzerland
4 Bern, Switzerland
5 Lausanne, Switzerland
6 Reykjavik, Iceland
7 Stavanger, Norway
8 Lugano, Switzerland
9 Oslo, Norway
10 Trondheim, Norway
11 Bergen, Norway
12 Kyoto, Japan
13 New York, NY, United States
14 Nassau, Bahamas
15 San Francisco, CA, United States
16 Copenhagen, Denmark
17 Luxembourg, Luxembourg
18 Anchorage, AK, United States
19 Honolulu, HI, United States
20 Tokyo, Japan
21 Brooklyn, NY, United States
22 Paris, France
23 Limerick, Ireland
24 Rockville, MD, United States
25 Bloomington, IN, United States
26 Washington, DC, United States
27 Arhus, Denmark
28 Singapore, Singapore
29 Aalborg, Denmark
...
508 Lahore, Pakistan
509 Pristina, Kosovo (Disputed Territory)
510 Chandigarh, India
511 Ahmedabad, India
512 Surat, India
513 Chennai, India
514 Goa, India
515 Indore, India
516 Kolkata, India
517 Lucknow (Lakhnau), India
518 Kiev, Ukraine
519 Jaipur, India
520 Karachi, Pakistan
521 Hyderabad, India
522 Cairo, Egypt
523 Dnipro, Ukraine
524 Nagpur, India
525 Bhopal, India
526 Vadodara, India
527 Mangalore, India
528 Lviv, Ukraine
529 Mysore, India
530 Bhubaneswar, India
531 Kharkiv, Ukraine
532 Visakhapatnam, India
533 Kochi, India
534 Coimbatore, India
535 Alexandria, Egypt
536 Navi Mumbai, India
537 Thiruvananthapuram, India
Name: City, Length: 538, dtype: object
python
df[['City','Restaurant Price Index']]
text
City Restaurant Price Index
0 Hamilton, Bermuda 158.75
1 Zurich, Switzerland 135.76
2 Geneva, Switzerland 129.74
3 Basel, Switzerland 127.22
4 Bern, Switzerland 119.48
5 Lausanne, Switzerland 132.12
6 Reykjavik, Iceland 133.19
7 Stavanger, Norway 143.54
8 Lugano, Switzerland 122.30
9 Oslo, Norway 124.09
10 Trondheim, Norway 134.76
11 Bergen, Norway 119.61
12 Kyoto, Japan 54.59
13 New York, NY, United States 100.00
14 Nassau, Bahamas 104.17
15 San Francisco, CA, United States 91.06
16 Copenhagen, Denmark 121.23
17 Luxembourg, Luxembourg 109.61
18 Anchorage, AK, United States 84.55
19 Honolulu, HI, United States 82.86
20 Tokyo, Japan 58.93
21 Brooklyn, NY, United States 100.58
22 Paris, France 91.77
23 Limerick, Ireland 82.93
24 Rockville, MD, United States 74.74
25 Bloomington, IN, United States 75.43
26 Washington, DC, United States 85.00
27 Arhus, Denmark 102.82
28 Singapore, Singapore 64.40
29 Aalborg, Denmark 101.14
.. ... ...
508 Lahore, Pakistan 26.39
509 Pristina, Kosovo (Disputed Territory) 22.78
510 Chandigarh, India 20.18
511 Ahmedabad, India 20.13
512 Surat, India 19.84
513 Chennai, India 18.26
514 Goa, India 22.96
515 Indore, India 17.77
516 Kolkata, India 23.18
517 Lucknow (Lakhnau), India 18.76
518 Kiev, Ukraine 22.01
519 Jaipur, India 18.48
520 Karachi, Pakistan 21.62
521 Hyderabad, India 18.93
522 Cairo, Egypt 22.55
523 Dnipro, Ukraine 22.74
524 Nagpur, India 18.73
525 Bhopal, India 16.21
526 Vadodara, India 16.02
527 Mangalore, India 16.04
528 Lviv, Ukraine 17.88
529 Mysore, India 13.31
530 Bhubaneswar, India 14.91
531 Kharkiv, Ukraine 18.44
532 Visakhapatnam, India 18.07
533 Kochi, India 13.94
534 Coimbatore, India 15.21
535 Alexandria, Egypt 17.66
536 Navi Mumbai, India 14.14
537 Thiruvananthapuram, India 12.06
[538 rows x 2 columns]
python
df[2:10] #specific rows, all columns
text
Rank City Cost of Living Index Rent Index \
2 3 Geneva, Switzerland 134.83 71.70
3 4 Basel, Switzerland 130.68 49.68
4 5 Bern, Switzerland 128.03 43.57
5 6 Lausanne, Switzerland 127.50 52.32
6 7 Reykjavik, Iceland 123.78 57.25
7 8 Stavanger, Norway 118.61 39.83
8 9 Lugano, Switzerland 118.24 52.91
9 10 Oslo, Norway 117.23 49.28
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
5 91.24 126.59 132.12
6 91.70 118.15 133.19
7 80.61 106.09 143.54
8 86.73 117.74 122.30
9 84.46 112.42 124.09
Local Purchasing Power Index
2 130.96
3 139.01
4 112.71
5 127.95
6 88.95
7 118.14
8 119.86
9 102.94
python
#.at labels based
df.at[3,'Rent Index']
text
49.68
python
#.iat integer based
df.iat[3,3]
text
49.68
python
df.head(10)
text
Rank City Cost of Living Index Rent Index \
0 1 Hamilton, Bermuda 145.43 110.87
1 2 Zurich, Switzerland 141.25 66.14
2 3 Geneva, Switzerland 134.83 71.70
3 4 Basel, Switzerland 130.68 49.68
4 5 Bern, Switzerland 128.03 43.57
5 6 Lausanne, Switzerland 127.50 52.32
6 7 Reykjavik, Iceland 123.78 57.25
7 8 Stavanger, Norway 118.61 39.83
8 9 Lugano, Switzerland 118.24 52.91
9 10 Oslo, Norway 117.23 49.28
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
0 128.76 143.47 158.75
1 105.03 149.86 135.76
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
5 91.24 126.59 132.12
6 91.70 118.15 133.19
7 80.61 106.09 143.54
8 86.73 117.74 122.30
9 84.46 112.42 124.09
Local Purchasing Power Index
0 112.26
1 142.70
2 130.96
3 139.01
4 112.71
5 127.95
6 88.95
7 118.14
8 119.86
9 102.94
python
#loc is label based
python
#select specific rows and column
df.loc[:,['City', 'Cost of Living Index', 'Rent Index',
'Cost of Living Plus Rent Index', 'Groceries Index',
'Restaurant Price Index', 'Local Purchasing Power Index']]
text
City Cost of Living Index Rent Index \
0 Hamilton, Bermuda 145.43 110.87
1 Zurich, Switzerland 141.25 66.14
2 Geneva, Switzerland 134.83 71.70
3 Basel, Switzerland 130.68 49.68
4 Bern, Switzerland 128.03 43.57
5 Lausanne, Switzerland 127.50 52.32
6 Reykjavik, Iceland 123.78 57.25
7 Stavanger, Norway 118.61 39.83
8 Lugano, Switzerland 118.24 52.91
9 Oslo, Norway 117.23 49.28
10 Trondheim, Norway 114.22 42.39
11 Bergen, Norway 112.31 40.30
12 Kyoto, Japan 100.33 24.58
13 New York, NY, United States 100.00 100.00
14 Nassau, Bahamas 99.73 40.45
15 San Francisco, CA, United States 97.84 115.36
16 Copenhagen, Denmark 97.62 50.66
17 Luxembourg, Luxembourg 95.37 61.59
18 Anchorage, AK, United States 94.99 40.12
19 Honolulu, HI, United States 94.15 62.82
20 Tokyo, Japan 93.81 37.07
21 Brooklyn, NY, United States 93.79 76.24
22 Paris, France 92.87 50.30
23 Limerick, Ireland 92.73 27.71
24 Rockville, MD, United States 92.66 64.00
25 Bloomington, IN, United States 92.14 33.64
26 Washington, DC, United States 91.94 73.30
27 Arhus, Denmark 91.90 34.82
28 Singapore, Singapore 91.40 71.89
29 Aalborg, Denmark 91.17 26.81
.. ... ... ...
508 Lahore, Pakistan 29.53 6.67
509 Pristina, Kosovo (Disputed Territory) 29.25 9.38
510 Chandigarh, India 29.04 6.47
511 Ahmedabad, India 28.67 6.24
512 Surat, India 28.66 4.69
513 Chennai, India 28.42 7.12
514 Goa, India 28.30 8.27
515 Indore, India 28.06 4.66
516 Kolkata, India 27.99 7.77
517 Lucknow (Lakhnau), India 27.55 4.90
518 Kiev, Ukraine 27.52 12.43
519 Jaipur, India 27.11 5.19
520 Karachi, Pakistan 27.10 7.46
521 Hyderabad, India 26.92 6.89
522 Cairo, Egypt 26.49 5.43
523 Dnipro, Ukraine 26.39 6.63
524 Nagpur, India 26.23 4.96
525 Bhopal, India 26.07 4.13
526 Vadodara, India 25.59 4.01
527 Mangalore, India 25.46 5.70
528 Lviv, Ukraine 25.31 8.10
529 Mysore, India 25.20 4.01
530 Bhubaneswar, India 24.89 4.68
531 Kharkiv, Ukraine 24.85 8.29
532 Visakhapatnam, India 24.66 4.85
533 Kochi, India 24.65 6.31
534 Coimbatore, India 24.61 5.35
535 Alexandria, Egypt 23.78 4.34
536 Navi Mumbai, India 23.44 6.25
537 Thiruvananthapuram, India 20.86 5.10
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
0 128.76 143.47 158.75
1 105.03 149.86 135.76
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
5 91.24 126.59 132.12
6 91.70 118.15 133.19
7 80.61 106.09 143.54
8 86.73 117.74 122.30
9 84.46 112.42 124.09
10 79.58 103.50 134.76
11 77.58 101.79 119.61
12 63.80 118.44 54.59
13 100.00 100.00 100.00
14 71.14 85.34 104.17
15 106.29 107.52 91.06
16 74.97 77.53 121.23
17 79.08 82.71 109.61
18 68.53 101.18 84.55
19 79.04 104.69 82.86
20 66.45 99.67 58.93
21 85.33 92.73 100.58
22 72.34 87.29 91.77
23 61.37 87.15 82.93
24 78.84 87.76 74.74
25 63.93 112.83 75.43
26 82.95 92.74 85.00
27 64.37 71.50 102.82
28 81.99 83.64 64.40
29 60.13 73.79 101.14
.. ... ... ...
508 18.50 26.83 26.39
509 19.67 25.97 22.78
510 18.15 29.40 20.18
511 17.85 31.42 20.13
512 17.10 31.97 19.84
513 18.14 31.17 18.26
514 18.64 29.80 22.96
515 16.78 27.74 17.77
516 18.24 28.53 23.18
517 16.62 27.25 18.76
518 20.24 21.96 22.01
519 16.54 27.65 18.48
520 17.63 25.60 21.62
521 17.26 27.60 18.93
522 16.33 23.23 22.55
523 16.86 20.46 22.74
524 15.97 26.55 18.73
525 15.49 22.49 16.21
526 15.18 27.85 16.02
527 15.93 26.85 16.04
528 17.01 20.50 17.88
529 14.98 29.39 13.31
530 15.14 28.22 14.91
531 16.87 19.26 18.44
532 15.11 25.83 18.07
533 15.80 26.93 13.94
534 15.32 25.23 15.21
535 14.40 23.19 17.66
536 15.15 24.02 14.14
537 13.26 21.98 12.06
Local Purchasing Power Index
0 112.26
1 142.70
2 130.96
3 139.01
4 112.71
5 127.95
6 88.95
7 118.14
8 119.86
9 102.94
10 108.29
11 99.29
12 77.92
13 100.00
14 58.69
15 92.96
16 113.31
17 127.42
18 124.92
19 103.08
20 106.42
21 87.04
22 97.62
23 93.93
24 130.79
25 96.92
26 120.62
27 109.47
28 95.89
29 106.35
.. ...
508 51.44
509 64.57
510 68.83
511 73.59
512 57.84
513 72.34
514 54.55
515 50.42
516 56.30
517 76.10
518 37.48
519 76.50
520 39.06
521 80.90
522 25.27
523 31.06
524 95.19
525 66.21
526 80.63
527 94.53
528 26.88
529 42.49
530 57.56
531 27.19
532 63.97
533 77.70
534 53.23
535 23.75
536 111.99
537 66.25
[538 rows x 7 columns]
python
#select all rows but specific column
df.loc[[0,3],['City', 'Cost of Living Index', 'Rent Index',
'Cost of Living Plus Rent Index', 'Groceries Index',
'Restaurant Price Index', 'Local Purchasing Power Index']]
text
City Cost of Living Index Rent Index \
0 Hamilton, Bermuda 145.43 110.87
3 Basel, Switzerland 130.68 49.68
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
0 128.76 143.47 158.75
3 91.61 127.54 127.22
Local Purchasing Power Index
0 112.26
3 139.01
python
#iloc is integer based
python
#Specific rows but all columns, remember here last index number is excluding
df.iloc[:5]
Rank City Cost of Living Index Rent Index \
0 1 Hamilton, Bermuda 145.43 110.87
1 2 Zurich, Switzerland 141.25 66.14
2 3 Geneva, Switzerland 134.83 71.70
3 4 Basel, Switzerland 130.68 49.68
4 5 Bern, Switzerland 128.03 43.57
Cost of Living Plus Rent Index Groceries Index Restaurant Price Index \
0 128.76 143.47 158.75
1 105.03 149.86 135.76
2 104.38 138.98 129.74
3 91.61 127.54 127.22
4 87.30 132.70 119.48
Local Purchasing Power Index
0 112.26
1 142.70
2 130.96
3 139.01
4 112.71
python
#Select all rows and specific column, remember here last index number is excluding
df.iloc[:,2:5]
text
Cost of Living Index Rent Index Cost of Living Plus Rent Index
0 145.43 110.87 128.76
1 141.25 66.14 105.03
2 134.83 71.70 104.38
3 130.68 49.68 91.61
4 128.03 43.57 87.30
5 127.50 52.32 91.24
6 123.78 57.25 91.70
7 118.61 39.83 80.61
8 118.24 52.91 86.73
9 117.23 49.28 84.46
10 114.22 42.39 79.58
11 112.31 40.30 77.58
12 100.33 24.58 63.80
13 100.00 100.00 100.00
14 99.73 40.45 71.14
15 97.84 115.36 106.29
16 97.62 50.66 74.97
17 95.37 61.59 79.08
18 94.99 40.12 68.53
19 94.15 62.82 79.04
20 93.81 37.07 66.45
21 93.79 76.24 85.33
22 92.87 50.30 72.34
23 92.73 27.71 61.37
24 92.66 64.00 78.84
25 92.14 33.64 63.93
26 91.94 73.30 82.95
27 91.90 34.82 64.37
28 91.40 71.89 81.99
29 91.17 26.81 60.13
.. ... ... ...
508 29.53 6.67 18.50
509 29.25 9.38 19.67
510 29.04 6.47 18.15
511 28.67 6.24 17.85
512 28.66 4.69 17.10
513 28.42 7.12 18.14
514 28.30 8.27 18.64
515 28.06 4.66 16.78
516 27.99 7.77 18.24
517 27.55 4.90 16.62
518 27.52 12.43 20.24
519 27.11 5.19 16.54
520 27.10 7.46 17.63
521 26.92 6.89 17.26
522 26.49 5.43 16.33
523 26.39 6.63 16.86
524 26.23 4.96 15.97
525 26.07 4.13 15.49
526 25.59 4.01 15.18
527 25.46 5.70 15.93
528 25.31 8.10 17.01
529 25.20 4.01 14.98
530 24.89 4.68 15.14
531 24.85 8.29 16.87
532 24.66 4.85 15.11
533 24.65 6.31 15.80
534 24.61 5.35 15.32
535 23.78 4.34 14.40
536 23.44 6.25 15.15
537 20.86 5.10 13.26
[538 rows x 3 columns]