Python import data, indexing, slicing

Updated by Faisal Akbar 18 min read
Table of contents

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 packagename
conda install package
name
use pip in command prompt
conda in Anaconda prompt

Working Directory

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

Change directory

%cd C:\Users\faisal\Desktop\Python\Lesson-1 #cd- change directory
[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

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

Import csv from Local Machine

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')
df.head()
   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

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

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

Output Excel

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

Import from SQL Server:

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)
df_sql.head()
   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

df_html=pd.read_html('https://www.contextures.com/xlSampleData01.html',header=0)
df_html[0].head()
   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

df_git = pd.read_csv('https://raw.githubusercontent.com/cs109/2014_data/master/mtcars.csv')
df_git.head()
          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
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

df.shape
(538, 8)
df.columns
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')
df.info()
<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
df.count()
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
df.sum()
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
df.min()
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
df.max()
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
df.describe()
             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
df.mean()
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
df.median()
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
#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
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

df['City']
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
df[['City','Restaurant Price Index']]
                                      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]
df[2:10] #specific rows, all columns
   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
#.at labels based
df.at[3,'Rent Index']
49.68
#.iat integer based
df.iat[3,3]
49.68
df.head(10)
   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
#loc is label based
#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']]
                                      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]
#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']]
                 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
#iloc is integer based
#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
#Select all rows and specific column, remember here last index number is excluding
df.iloc[:,2:5]
     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]