How to compare 3 columns of DataFrame together, Python 3.6

I have below dataframe and I want to compare 3 columns value & update True/False in another column "Id_Name_Table_Matching"

Below my code:

L1_ID = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Name = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Table = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']



DF1 = pd.DataFrame({'dimId': L1_ID, 'dimName': L1_Name, 'sqlTableColumn': L1_Table})

I want to update true in "Id_Name_Table_Matching" if all columns value matches else false.
I need script like below:

DF1['Id_Name_Table_Matching'] = DF1['dimId'] == DF1['dimName'] == DF1['sqlTableColumn']

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

add a comment |

I have below dataframe and I want to compare 3 columns value & update True/False in another column "Id_Name_Table_Matching"

Below my code:

L1_ID = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Name = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Table = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']



DF1 = pd.DataFrame({'dimId': L1_ID, 'dimName': L1_Name, 'sqlTableColumn': L1_Table})

I want to update true in "Id_Name_Table_Matching" if all columns value matches else false.
I need script like below:

DF1['Id_Name_Table_Matching'] = DF1['dimId'] == DF1['dimName'] == DF1['sqlTableColumn']

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

add a comment |

I have below dataframe and I want to compare 3 columns value & update True/False in another column "Id_Name_Table_Matching"

Below my code:

L1_ID = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Name = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Table = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']



DF1 = pd.DataFrame({'dimId': L1_ID, 'dimName': L1_Name, 'sqlTableColumn': L1_Table})

I want to update true in "Id_Name_Table_Matching" if all columns value matches else false.
I need script like below:

DF1['Id_Name_Table_Matching'] = DF1['dimId'] == DF1['dimName'] == DF1['sqlTableColumn']

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

I have below dataframe and I want to compare 3 columns value & update True/False in another column "Id_Name_Table_Matching"

Below my code:

L1_ID = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Name = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']

L1_Table = ['Region', 'Col2', 'Col3', 'Col4', 'Col5']



DF1 = pd.DataFrame({'dimId': L1_ID, 'dimName': L1_Name, 'sqlTableColumn': L1_Table})

I want to update true in "Id_Name_Table_Matching" if all columns value matches else false.
I need script like below:

DF1['Id_Name_Table_Matching'] = DF1['dimId'] == DF1['dimName'] == DF1['sqlTableColumn']

python python-3.x pandas list dataframe

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

edited Nov 13 '18 at 14:10

asked Nov 13 '18 at 14:06

SPy

497519

asked Nov 13 '18 at 14:06

SPy

497519

asked Nov 13 '18 at 14:06

SPy

497519

add a comment |

2 Answers
2

active

oldest

votes

Compare first columns with second, then with last and chain boolena masks by & for bitwise AND:

DF1['Id_Name_Table_Matching'] = (DF1['dimId'] == DF1['dimName']) & 

                                (DF1['dimId'] == DF1['sqlTableColumn'])

General solution for compare multiple columns defined in list - all filtered columns compare by first one by DataFrame.eq and then check if all values per rows are Trues by DataFrame.all:

cols = ['dimId','dimName','sqlTableColumn']

DF1['Id_Name_Table_Matching'] = DF1[cols].eq(DF1[cols[0]], axis=0).all(axis=1)

print (DF1)

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

Detail:

print (DF1[cols].eq(DF1[cols[0]], axis=0))

   dimId  dimName  sqlTableColumn

0   True     True            True

1   True     True            True

2   True     True            True

3   True     True            True

4   True     True            True

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

1

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

add a comment |

Look if this helps. Using .apply()

df["Id_Name_Table_Matching"] = df.apply(lambda x: x.dimId == x.dimName == x.sqlTableColumn, axis = 1)

print(df)

Output:

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

Compare first columns with second, then with last and chain boolena masks by & for bitwise AND:

DF1['Id_Name_Table_Matching'] = (DF1['dimId'] == DF1['dimName']) & 

                                (DF1['dimId'] == DF1['sqlTableColumn'])

General solution for compare multiple columns defined in list - all filtered columns compare by first one by DataFrame.eq and then check if all values per rows are Trues by DataFrame.all:

cols = ['dimId','dimName','sqlTableColumn']

DF1['Id_Name_Table_Matching'] = DF1[cols].eq(DF1[cols[0]], axis=0).all(axis=1)

print (DF1)

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

Detail:

print (DF1[cols].eq(DF1[cols[0]], axis=0))

   dimId  dimName  sqlTableColumn

0   True     True            True

1   True     True            True

2   True     True            True

3   True     True            True

4   True     True            True

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

1

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

add a comment |

Compare first columns with second, then with last and chain boolena masks by & for bitwise AND:

DF1['Id_Name_Table_Matching'] = (DF1['dimId'] == DF1['dimName']) & 

                                (DF1['dimId'] == DF1['sqlTableColumn'])

General solution for compare multiple columns defined in list - all filtered columns compare by first one by DataFrame.eq and then check if all values per rows are Trues by DataFrame.all:

cols = ['dimId','dimName','sqlTableColumn']

DF1['Id_Name_Table_Matching'] = DF1[cols].eq(DF1[cols[0]], axis=0).all(axis=1)

print (DF1)

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

Detail:

print (DF1[cols].eq(DF1[cols[0]], axis=0))

   dimId  dimName  sqlTableColumn

0   True     True            True

1   True     True            True

2   True     True            True

3   True     True            True

4   True     True            True

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

1

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

add a comment |

Compare first columns with second, then with last and chain boolena masks by & for bitwise AND:

DF1['Id_Name_Table_Matching'] = (DF1['dimId'] == DF1['dimName']) & 

                                (DF1['dimId'] == DF1['sqlTableColumn'])

General solution for compare multiple columns defined in list - all filtered columns compare by first one by DataFrame.eq and then check if all values per rows are Trues by DataFrame.all:

cols = ['dimId','dimName','sqlTableColumn']

DF1['Id_Name_Table_Matching'] = DF1[cols].eq(DF1[cols[0]], axis=0).all(axis=1)

print (DF1)

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

Detail:

print (DF1[cols].eq(DF1[cols[0]], axis=0))

   dimId  dimName  sqlTableColumn

0   True     True            True

1   True     True            True

2   True     True            True

3   True     True            True

4   True     True            True

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

Compare first columns with second, then with last and chain boolena masks by & for bitwise AND:

DF1['Id_Name_Table_Matching'] = (DF1['dimId'] == DF1['dimName']) & 

                                (DF1['dimId'] == DF1['sqlTableColumn'])

General solution for compare multiple columns defined in list - all filtered columns compare by first one by DataFrame.eq and then check if all values per rows are Trues by DataFrame.all:

cols = ['dimId','dimName','sqlTableColumn']

DF1['Id_Name_Table_Matching'] = DF1[cols].eq(DF1[cols[0]], axis=0).all(axis=1)

print (DF1)

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

Detail:

print (DF1[cols].eq(DF1[cols[0]], axis=0))

   dimId  dimName  sqlTableColumn

0   True     True            True

1   True     True            True

2   True     True            True

3   True     True            True

4   True     True            True

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

edited Nov 13 '18 at 14:17

answered Nov 13 '18 at 14:12

jezrael

329k23270349

answered Nov 13 '18 at 14:12

jezrael

329k23270349

answered Nov 13 '18 at 14:12

jezrael

329k23270349

1

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

add a comment |

1

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

thanks, First one ok - easy one

– SPy
Nov 13 '18 at 14:20

add a comment |

Look if this helps. Using .apply()

df["Id_Name_Table_Matching"] = df.apply(lambda x: x.dimId == x.dimName == x.sqlTableColumn, axis = 1)

print(df)

Output:

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

add a comment |

Look if this helps. Using .apply()

df["Id_Name_Table_Matching"] = df.apply(lambda x: x.dimId == x.dimName == x.sqlTableColumn, axis = 1)

print(df)

Output:

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

add a comment |

Look if this helps. Using .apply()

df["Id_Name_Table_Matching"] = df.apply(lambda x: x.dimId == x.dimName == x.sqlTableColumn, axis = 1)

print(df)

Output:

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

Look if this helps. Using .apply()

df["Id_Name_Table_Matching"] = df.apply(lambda x: x.dimId == x.dimName == x.sqlTableColumn, axis = 1)

print(df)

Output:

    dimId dimName sqlTableColumn  Id_Name_Table_Matching

0  Region  Region         Region                    True

1    Col2    Col2           Col2                    True

2    Col3    Col3           Col3                    True

3    Col4    Col4           Col4                    True

4    Col5    Col5           Col5                    True

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

edited Nov 29 '18 at 18:11

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

answered Nov 13 '18 at 14:38

Srce Cde

1,144511

add a comment |

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