How to write csv from two DataFrame in python

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I am trying to write data in expected data format. I have two data frames and want to write as per expected output format.call

Below are some examples and data frame details

first Dataframe -

df_1

  0          1                 2  3  4  5  6   7  8  9   10

0  1  DURVANKUR  Durvankur A wing     0  1  0  21  1  0  30

  0          1                 2  3  4  5  6   7  8  9   10

0  2  DURVANKUR  Durvankur B wing     0  1  0  15  1  0  70

Second DataFrame -

df_2

   0        1      2   3   4

0  1  1.5 BHK  44.57  1   1 

1  2  1.5 BHK  38.09  1   1 

2  3    2 BHK  51.01  4   2 

3  4    2 BHK  61.21  4   3 

4  5  2.5 BHK  43.16  1   1 

5  6    1 BHK  31.97  1   1 

6  7  1.5 BHK  37.30  2   2 

7  8  1.5 BHK  41.12  1   1 

   0        1       2    3    4

0  1    2 BHK   85.73  15   14 

1  2    2 BHK   92.27  15   15 

2  3  3.5 BHK  126.20   2    2

Code:

url_row_index=0



url = each.split(separator)[url_row_index] 



df_url = pd.read_csv(pd.compat.StringIO(url), header=None)



html=requests.get(url).content

soup=BeautifulSoup(html, 'lxml') 





table = soup.find_all('table')[3]    



data = 



for row in table.find_all("tr", recursive=False)[1:]:

    row_1 = [cell.get_text(strip=True) for cell in row.find_all("td")]

    data.append(row_1)



for i in range(0, len(data), 4):

    tableRows = data[i]

    df = pd.DataFrame(tableRows)

    df = df.T

    print (df)



    count_table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})



    for j in range(2, len(count_table_1), 1):



        table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})[j]

        Table_1_data = [[td.text for td in row.find_all("td")] for row in table_1.find_all("tr")[1:]]

        df_1 = pd.DataFrame(Table_1_data)

        print (df_1)



        df_2 = pd.concat([df_url, df, df_1], axis=1)

        df_2.fillna(method='ffill', inplace=True)

Current output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   2 BHK   85.73   15  14

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   2 BHK   92.27   15  15

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   3.5 BHK 126.2   2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   1.5 BHK 44.57   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   1.5 BHK 38.09   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   2 BHK   51.01   4   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  4   2 BHK   61.21   4   3

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  5   2.5 BHK 43.16   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  6   1 BHK   31.97   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  7   1.5 BHK 37.3    2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

Expected output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

add a comment |

up vote
0
down vote

favorite

I am trying to write data in expected data format. I have two data frames and want to write as per expected output format.call

Below are some examples and data frame details

first Dataframe -

df_1

  0          1                 2  3  4  5  6   7  8  9   10

0  1  DURVANKUR  Durvankur A wing     0  1  0  21  1  0  30

  0          1                 2  3  4  5  6   7  8  9   10

0  2  DURVANKUR  Durvankur B wing     0  1  0  15  1  0  70

Second DataFrame -

df_2

   0        1      2   3   4

0  1  1.5 BHK  44.57  1   1 

1  2  1.5 BHK  38.09  1   1 

2  3    2 BHK  51.01  4   2 

3  4    2 BHK  61.21  4   3 

4  5  2.5 BHK  43.16  1   1 

5  6    1 BHK  31.97  1   1 

6  7  1.5 BHK  37.30  2   2 

7  8  1.5 BHK  41.12  1   1 

   0        1       2    3    4

0  1    2 BHK   85.73  15   14 

1  2    2 BHK   92.27  15   15 

2  3  3.5 BHK  126.20   2    2

Code:

url_row_index=0



url = each.split(separator)[url_row_index] 



df_url = pd.read_csv(pd.compat.StringIO(url), header=None)



html=requests.get(url).content

soup=BeautifulSoup(html, 'lxml') 





table = soup.find_all('table')[3]    



data = 



for row in table.find_all("tr", recursive=False)[1:]:

    row_1 = [cell.get_text(strip=True) for cell in row.find_all("td")]

    data.append(row_1)



for i in range(0, len(data), 4):

    tableRows = data[i]

    df = pd.DataFrame(tableRows)

    df = df.T

    print (df)



    count_table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})



    for j in range(2, len(count_table_1), 1):



        table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})[j]

        Table_1_data = [[td.text for td in row.find_all("td")] for row in table_1.find_all("tr")[1:]]

        df_1 = pd.DataFrame(Table_1_data)

        print (df_1)



        df_2 = pd.concat([df_url, df, df_1], axis=1)

        df_2.fillna(method='ffill', inplace=True)

Current output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   2 BHK   85.73   15  14

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   2 BHK   92.27   15  15

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   3.5 BHK 126.2   2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   1.5 BHK 44.57   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   1.5 BHK 38.09   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   2 BHK   51.01   4   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  4   2 BHK   61.21   4   3

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  5   2.5 BHK 43.16   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  6   1 BHK   31.97   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  7   1.5 BHK 37.3    2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

Expected output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

add a comment |

up vote
0
down vote

favorite

I am trying to write data in expected data format. I have two data frames and want to write as per expected output format.call

Below are some examples and data frame details

first Dataframe -

df_1

  0          1                 2  3  4  5  6   7  8  9   10

0  1  DURVANKUR  Durvankur A wing     0  1  0  21  1  0  30

  0          1                 2  3  4  5  6   7  8  9   10

0  2  DURVANKUR  Durvankur B wing     0  1  0  15  1  0  70

Second DataFrame -

df_2

   0        1      2   3   4

0  1  1.5 BHK  44.57  1   1 

1  2  1.5 BHK  38.09  1   1 

2  3    2 BHK  51.01  4   2 

3  4    2 BHK  61.21  4   3 

4  5  2.5 BHK  43.16  1   1 

5  6    1 BHK  31.97  1   1 

6  7  1.5 BHK  37.30  2   2 

7  8  1.5 BHK  41.12  1   1 

   0        1       2    3    4

0  1    2 BHK   85.73  15   14 

1  2    2 BHK   92.27  15   15 

2  3  3.5 BHK  126.20   2    2

Code:

url_row_index=0



url = each.split(separator)[url_row_index] 



df_url = pd.read_csv(pd.compat.StringIO(url), header=None)



html=requests.get(url).content

soup=BeautifulSoup(html, 'lxml') 





table = soup.find_all('table')[3]    



data = 



for row in table.find_all("tr", recursive=False)[1:]:

    row_1 = [cell.get_text(strip=True) for cell in row.find_all("td")]

    data.append(row_1)



for i in range(0, len(data), 4):

    tableRows = data[i]

    df = pd.DataFrame(tableRows)

    df = df.T

    print (df)



    count_table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})



    for j in range(2, len(count_table_1), 1):



        table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})[j]

        Table_1_data = [[td.text for td in row.find_all("td")] for row in table_1.find_all("tr")[1:]]

        df_1 = pd.DataFrame(Table_1_data)

        print (df_1)



        df_2 = pd.concat([df_url, df, df_1], axis=1)

        df_2.fillna(method='ffill', inplace=True)

Current output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   2 BHK   85.73   15  14

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   2 BHK   92.27   15  15

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   3.5 BHK 126.2   2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   1.5 BHK 44.57   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   1.5 BHK 38.09   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   2 BHK   51.01   4   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  4   2 BHK   61.21   4   3

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  5   2.5 BHK 43.16   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  6   1 BHK   31.97   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  7   1.5 BHK 37.3    2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

Expected output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

I am trying to write data in expected data format. I have two data frames and want to write as per expected output format.call

Below are some examples and data frame details

first Dataframe -

df_1

  0          1                 2  3  4  5  6   7  8  9   10

0  1  DURVANKUR  Durvankur A wing     0  1  0  21  1  0  30

  0          1                 2  3  4  5  6   7  8  9   10

0  2  DURVANKUR  Durvankur B wing     0  1  0  15  1  0  70

Second DataFrame -

df_2

   0        1      2   3   4

0  1  1.5 BHK  44.57  1   1 

1  2  1.5 BHK  38.09  1   1 

2  3    2 BHK  51.01  4   2 

3  4    2 BHK  61.21  4   3 

4  5  2.5 BHK  43.16  1   1 

5  6    1 BHK  31.97  1   1 

6  7  1.5 BHK  37.30  2   2 

7  8  1.5 BHK  41.12  1   1 

   0        1       2    3    4

0  1    2 BHK   85.73  15   14 

1  2    2 BHK   92.27  15   15 

2  3  3.5 BHK  126.20   2    2

Code:

url_row_index=0



url = each.split(separator)[url_row_index] 



df_url = pd.read_csv(pd.compat.StringIO(url), header=None)



html=requests.get(url).content

soup=BeautifulSoup(html, 'lxml') 





table = soup.find_all('table')[3]    



data = 



for row in table.find_all("tr", recursive=False)[1:]:

    row_1 = [cell.get_text(strip=True) for cell in row.find_all("td")]

    data.append(row_1)



for i in range(0, len(data), 4):

    tableRows = data[i]

    df = pd.DataFrame(tableRows)

    df = df.T

    print (df)



    count_table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})



    for j in range(2, len(count_table_1), 1):



        table_1 = soup.find_all("table",{"class":"table table-bordered table-responsive table-striped"})[j]

        Table_1_data = [[td.text for td in row.find_all("td")] for row in table_1.find_all("tr")[1:]]

        df_1 = pd.DataFrame(Table_1_data)

        print (df_1)



        df_2 = pd.concat([df_url, df, df_1], axis=1)

        df_2.fillna(method='ffill', inplace=True)

Current output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   2 BHK   85.73   15  14

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   2 BHK   92.27   15  15

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   3.5 BHK 126.2   2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   1.5 BHK 44.57   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   1.5 BHK 38.09   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   2 BHK   51.01   4   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  4   2 BHK   61.21   4   3

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  5   2.5 BHK 43.16   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  6   1 BHK   31.97   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  7   1.5 BHK 37.3    2   2

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

Expected output:

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  1   1.5 BHK 44.57   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  2   1.5 BHK 38.09   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  3   2 BHK   51.01   4   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  4   2 BHK   61.21   4   3

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  5   2.5 BHK 43.16   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  6   1 BHK   31.97   1   1

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  7   1.5 BHK 37.3    2   2

1   DURVANKUR   Durvankur A wing        0   1   0   21  1   0   30  8   1.5 BHK 41.12   1   1

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  1   2 BHK   85.73   15  14

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  2   2 BHK   92.27   15  15

2   DURVANKUR   Durvankur B wing        0   1   0   15  1   0   70  3   3.5 BHK 126.2   2   2

python python-3.x pandas

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

edited 21 hours ago

James Z

11.1k71735

edited 21 hours ago

James Z

11.1k71735

edited 21 hours ago

James Z

11.1k71735

asked 22 hours ago

user10468005

315

asked 22 hours ago

user10468005

315

asked 22 hours ago

user10468005

315

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