Why does pd.as_matrix() change the values and the number of decimal places from the original data frame?

up vote
-1
down vote

favorite

I have a dataframe that consists of two decimal values and an Id:

enter image description here

When I apply the as matrix function on the x and y values it yields an array that looks like this:

coords = df.as_matrix(columns=['x', 'y'])

coords

yields:

array([[ 0.0703843 ,  0.170845  ],

       [ 0.07022078,  0.17150128],

       [ 0.07208886,  0.17159163],

       ..., 

       [ 0.07162819,  0.17044404],

       [ 0.06951432,  0.17096308],

       [ 0.07104143,  0.17040137]])

This immediately seemed strange since the length of the decimal place were inconsistent but I just assumed pandas was doing some shortening for display purposes

But then when I tried to retrieve the IDs - I could only get one or zero matchs when they should all match:

ids = 

for coord in coords:

        try:

            _id = df.loc[df['x'] == coord[0]]['id'][1]

            ids.append(_id)

        except:                

            pass

len(ids)

1

What I am trying to understand is why the pd.as_matrix function extracts a value from the data frame that cannot be referenced again, and if so how do retrieve the ids from the data frame.

Any help here would be appreciated.

Thanks

Edit

Bellow is an subset of the data frame in CSV:

,id,x,y

0,07379a26-2447-4fce-83ac-4784abf07389,0.07038429591623253,0.17084500318384327

1,f5cc3adb-0588-4705-b1a3-fe1b669b776f,0.07022078416348305,0.17150127781674332

2,b5a57ffe-8565-4443-9685-11675ce25dc4,0.07208886125821728,0.17159163002146055

3,940efcaa-6d9d-4b10-a0fe-d8ec8c1d9c7e,0.07057468050347501,0.1700482708522834

4,616d7794-565a-4d2d-98cb-334beb5b91ef,0.07057895306948389,0.170054305037284

5,e2d1819d-1f58-407d-9950-be0a0c00374b,0.07161607658023798,0.17013089473907284

6,6a739687-f9ad-47bd-8a4b-c47bc4b2aec6,0.070163429153604,0.16889764101717875

7,dd2df646-9a66-4baa-8815-d24f1858eda7,0.07035099968831582,0.16995622800529742

8,6a224d76-efea-4313-803d-c25b619dae0a,0.07066777462044714,0.17021849979554743

9,321147fa-ee51-4bab-9634-199c92a42d2f,0.06984869509314469,0.17098101436534555

10,e52d6289-01ba-4e7d-8054-bb9a349c0505,0.07068704829137691,0.17029718331066224

11,517f256b-6171-4d93-9b4b-0f81aac828fb,0.0713283119291569,0.16983952831019206

12,e339c742-9784-49fc-a435-790db0364229,0.07131341496221469,0.1698513011377732

13,6f20ad5a-22fb-43a2-8885-838e5161df14,0.06942397329210678,0.1716572235671854

14,f6e1008f-2b22-4d88-8c84-c0dc4f2d822e,0.06942427697939664,0.17165098925109726

15,8a2d35e5-10a2-4188-b98f-54200d2db8da,0.07048162129308791,0.16896051533992895

16,adab8fd8-4348-412d-85d2-01491886967b,0.07076495746208027,0.16966622176968035

17,df79523b-848b-45a9-8dab-fe53c2a5b62d,0.06988926585338372,0.17028143287771583

18,db05d97c-3b16-4da8-9659-820fc7e3f858,0.0713167479593096,0.1685149810693375

19,d43963d1-b803-473c-85dc-2ed2e9f77f4e,0.07045583812582461,0.1706502407290604

20,9d99c9a6-2de3-4e6a-9bd7-9d7ece358a2f,0.07044174575566758,0.17066067488910522

21,3eec44be-b9e2-45a2-b919-05028f5a0ba9,0.07079585677115756,0.16920818686920963

22,9f836847-2b67-4b33-930a-1f84452628ba,0.07078522829778934,0.16919781903167638

23,fbaa8958-a5d5-4dfb-91f7-8c11afe226a8,0.07128542860765898,0.16834798505762455

24,a84b59c4-4145-472d-a26a-4c930648c16c,0.07196635776157265,0.17047633495883885

25,29cf8ad3-7068-4207-b0a2-4f0cff337c9f,0.0719701195278871,0.17051442269732875

26,d0f512c8-5c4f-427a-99e1-ebb4c5b363e5,0.0718787509597688,0.17054903897593635

27,74b1db2d-002b-4f89-8d02-ac084e9a3cd5,0.07089130417373782,0.16981103290127117

28,89210a0c-8144-491d-9e98-19e7f4c3085e,0.07076060461092577,0.1707011426749184

29,aebb377e-7c26-4bb5-8563-c3055a027844,0.07103977816965212,0.17113978347674103

30,00b527a0-d40a-44b4-90f9-750fd447d2d7,0.07097785505134419,0.16963542019904118

31,8c186559-f50d-40ca-a821-11596e1e5261,0.06992637446216321,0.17110063865050085

32,0e64cf14-6ccd-4ad0-9715-ab410f6baf6a,0.0718311255786932,0.1705675237580442

33,f5479823-1efe-47b8-9977-73dc41d1d69e,0.07016981880399553,0.1703708437681898

34,385cfa13-2476-4e3d-b755-3063a7f802b9,0.07016550435008462,0.17037054473511137

35,a40bf573-b701-46f0-9a06-5857cf3ab199,0.0701443567773146,0.17035314147536326

36,0c5a9751-2c1b-4003-834d-9584d2f907a2,0.07016050805421256,0.17038992836178396

37,65b09067-9cf0-492d-8a70-13d4f92f8a10,0.07137336818557355,0.1684713798357405

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

Also why are you using a bare except clause? What error is occoruing that you are ignoring?
– FHTMitchell
Nov 7 at 10:23

I just ran similar code (with manually copied dataframe and no try/except) and got no issues, every value matched. This sounds like a floating point rounding error. Try np.isclose rather than ==.
– FHTMitchell
Nov 7 at 10:27

@FHTMitchell Hi. The dataframe is not trivial to generate. I have included a subset in CSV. The error code is a key value error because the loc is returning an empty frame.
– Johny Mudly
Nov 7 at 12:02

@FHTMitchell Using a try except to see how many actually match.
– Johny Mudly
Nov 7 at 12:04

@FHTMitchell - You cant use np.isclose() in the df.loc function - so there is no way to locate it.
– Johny Mudly
Nov 7 at 12:32

add a comment |

up vote
-1
down vote

favorite

I have a dataframe that consists of two decimal values and an Id:

enter image description here

When I apply the as matrix function on the x and y values it yields an array that looks like this:

coords = df.as_matrix(columns=['x', 'y'])

coords

yields:

array([[ 0.0703843 ,  0.170845  ],

       [ 0.07022078,  0.17150128],

       [ 0.07208886,  0.17159163],

       ..., 

       [ 0.07162819,  0.17044404],

       [ 0.06951432,  0.17096308],

       [ 0.07104143,  0.17040137]])

This immediately seemed strange since the length of the decimal place were inconsistent but I just assumed pandas was doing some shortening for display purposes

But then when I tried to retrieve the IDs - I could only get one or zero matchs when they should all match:

ids = 

for coord in coords:

        try:

            _id = df.loc[df['x'] == coord[0]]['id'][1]

            ids.append(_id)

        except:                

            pass

len(ids)

1

What I am trying to understand is why the pd.as_matrix function extracts a value from the data frame that cannot be referenced again, and if so how do retrieve the ids from the data frame.

Any help here would be appreciated.

Thanks

Edit

Bellow is an subset of the data frame in CSV:

,id,x,y

0,07379a26-2447-4fce-83ac-4784abf07389,0.07038429591623253,0.17084500318384327

1,f5cc3adb-0588-4705-b1a3-fe1b669b776f,0.07022078416348305,0.17150127781674332

2,b5a57ffe-8565-4443-9685-11675ce25dc4,0.07208886125821728,0.17159163002146055

3,940efcaa-6d9d-4b10-a0fe-d8ec8c1d9c7e,0.07057468050347501,0.1700482708522834

4,616d7794-565a-4d2d-98cb-334beb5b91ef,0.07057895306948389,0.170054305037284

5,e2d1819d-1f58-407d-9950-be0a0c00374b,0.07161607658023798,0.17013089473907284

6,6a739687-f9ad-47bd-8a4b-c47bc4b2aec6,0.070163429153604,0.16889764101717875

7,dd2df646-9a66-4baa-8815-d24f1858eda7,0.07035099968831582,0.16995622800529742

8,6a224d76-efea-4313-803d-c25b619dae0a,0.07066777462044714,0.17021849979554743

9,321147fa-ee51-4bab-9634-199c92a42d2f,0.06984869509314469,0.17098101436534555

10,e52d6289-01ba-4e7d-8054-bb9a349c0505,0.07068704829137691,0.17029718331066224

11,517f256b-6171-4d93-9b4b-0f81aac828fb,0.0713283119291569,0.16983952831019206

12,e339c742-9784-49fc-a435-790db0364229,0.07131341496221469,0.1698513011377732

13,6f20ad5a-22fb-43a2-8885-838e5161df14,0.06942397329210678,0.1716572235671854

14,f6e1008f-2b22-4d88-8c84-c0dc4f2d822e,0.06942427697939664,0.17165098925109726

15,8a2d35e5-10a2-4188-b98f-54200d2db8da,0.07048162129308791,0.16896051533992895

16,adab8fd8-4348-412d-85d2-01491886967b,0.07076495746208027,0.16966622176968035

17,df79523b-848b-45a9-8dab-fe53c2a5b62d,0.06988926585338372,0.17028143287771583

18,db05d97c-3b16-4da8-9659-820fc7e3f858,0.0713167479593096,0.1685149810693375

19,d43963d1-b803-473c-85dc-2ed2e9f77f4e,0.07045583812582461,0.1706502407290604

20,9d99c9a6-2de3-4e6a-9bd7-9d7ece358a2f,0.07044174575566758,0.17066067488910522

21,3eec44be-b9e2-45a2-b919-05028f5a0ba9,0.07079585677115756,0.16920818686920963

22,9f836847-2b67-4b33-930a-1f84452628ba,0.07078522829778934,0.16919781903167638

23,fbaa8958-a5d5-4dfb-91f7-8c11afe226a8,0.07128542860765898,0.16834798505762455

24,a84b59c4-4145-472d-a26a-4c930648c16c,0.07196635776157265,0.17047633495883885

25,29cf8ad3-7068-4207-b0a2-4f0cff337c9f,0.0719701195278871,0.17051442269732875

26,d0f512c8-5c4f-427a-99e1-ebb4c5b363e5,0.0718787509597688,0.17054903897593635

27,74b1db2d-002b-4f89-8d02-ac084e9a3cd5,0.07089130417373782,0.16981103290127117

28,89210a0c-8144-491d-9e98-19e7f4c3085e,0.07076060461092577,0.1707011426749184

29,aebb377e-7c26-4bb5-8563-c3055a027844,0.07103977816965212,0.17113978347674103

30,00b527a0-d40a-44b4-90f9-750fd447d2d7,0.07097785505134419,0.16963542019904118

31,8c186559-f50d-40ca-a821-11596e1e5261,0.06992637446216321,0.17110063865050085

32,0e64cf14-6ccd-4ad0-9715-ab410f6baf6a,0.0718311255786932,0.1705675237580442

33,f5479823-1efe-47b8-9977-73dc41d1d69e,0.07016981880399553,0.1703708437681898

34,385cfa13-2476-4e3d-b755-3063a7f802b9,0.07016550435008462,0.17037054473511137

35,a40bf573-b701-46f0-9a06-5857cf3ab199,0.0701443567773146,0.17035314147536326

36,0c5a9751-2c1b-4003-834d-9584d2f907a2,0.07016050805421256,0.17038992836178396

37,65b09067-9cf0-492d-8a70-13d4f92f8a10,0.07137336818557355,0.1684713798357405

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

Also why are you using a bare except clause? What error is occoruing that you are ignoring?
– FHTMitchell
Nov 7 at 10:23

I just ran similar code (with manually copied dataframe and no try/except) and got no issues, every value matched. This sounds like a floating point rounding error. Try np.isclose rather than ==.
– FHTMitchell
Nov 7 at 10:27

@FHTMitchell Hi. The dataframe is not trivial to generate. I have included a subset in CSV. The error code is a key value error because the loc is returning an empty frame.
– Johny Mudly
Nov 7 at 12:02

@FHTMitchell Using a try except to see how many actually match.
– Johny Mudly
Nov 7 at 12:04

@FHTMitchell - You cant use np.isclose() in the df.loc function - so there is no way to locate it.
– Johny Mudly
Nov 7 at 12:32

add a comment |

up vote
-1
down vote

favorite

I have a dataframe that consists of two decimal values and an Id:

enter image description here

When I apply the as matrix function on the x and y values it yields an array that looks like this:

coords = df.as_matrix(columns=['x', 'y'])

coords

yields:

array([[ 0.0703843 ,  0.170845  ],

       [ 0.07022078,  0.17150128],

       [ 0.07208886,  0.17159163],

       ..., 

       [ 0.07162819,  0.17044404],

       [ 0.06951432,  0.17096308],

       [ 0.07104143,  0.17040137]])

This immediately seemed strange since the length of the decimal place were inconsistent but I just assumed pandas was doing some shortening for display purposes

But then when I tried to retrieve the IDs - I could only get one or zero matchs when they should all match:

ids = 

for coord in coords:

        try:

            _id = df.loc[df['x'] == coord[0]]['id'][1]

            ids.append(_id)

        except:                

            pass

len(ids)

1

What I am trying to understand is why the pd.as_matrix function extracts a value from the data frame that cannot be referenced again, and if so how do retrieve the ids from the data frame.

Any help here would be appreciated.

Thanks

Edit

Bellow is an subset of the data frame in CSV:

,id,x,y

0,07379a26-2447-4fce-83ac-4784abf07389,0.07038429591623253,0.17084500318384327

1,f5cc3adb-0588-4705-b1a3-fe1b669b776f,0.07022078416348305,0.17150127781674332

2,b5a57ffe-8565-4443-9685-11675ce25dc4,0.07208886125821728,0.17159163002146055

3,940efcaa-6d9d-4b10-a0fe-d8ec8c1d9c7e,0.07057468050347501,0.1700482708522834

4,616d7794-565a-4d2d-98cb-334beb5b91ef,0.07057895306948389,0.170054305037284

5,e2d1819d-1f58-407d-9950-be0a0c00374b,0.07161607658023798,0.17013089473907284

6,6a739687-f9ad-47bd-8a4b-c47bc4b2aec6,0.070163429153604,0.16889764101717875

7,dd2df646-9a66-4baa-8815-d24f1858eda7,0.07035099968831582,0.16995622800529742

8,6a224d76-efea-4313-803d-c25b619dae0a,0.07066777462044714,0.17021849979554743

9,321147fa-ee51-4bab-9634-199c92a42d2f,0.06984869509314469,0.17098101436534555

10,e52d6289-01ba-4e7d-8054-bb9a349c0505,0.07068704829137691,0.17029718331066224

11,517f256b-6171-4d93-9b4b-0f81aac828fb,0.0713283119291569,0.16983952831019206

12,e339c742-9784-49fc-a435-790db0364229,0.07131341496221469,0.1698513011377732

13,6f20ad5a-22fb-43a2-8885-838e5161df14,0.06942397329210678,0.1716572235671854

14,f6e1008f-2b22-4d88-8c84-c0dc4f2d822e,0.06942427697939664,0.17165098925109726

15,8a2d35e5-10a2-4188-b98f-54200d2db8da,0.07048162129308791,0.16896051533992895

16,adab8fd8-4348-412d-85d2-01491886967b,0.07076495746208027,0.16966622176968035

17,df79523b-848b-45a9-8dab-fe53c2a5b62d,0.06988926585338372,0.17028143287771583

18,db05d97c-3b16-4da8-9659-820fc7e3f858,0.0713167479593096,0.1685149810693375

19,d43963d1-b803-473c-85dc-2ed2e9f77f4e,0.07045583812582461,0.1706502407290604

20,9d99c9a6-2de3-4e6a-9bd7-9d7ece358a2f,0.07044174575566758,0.17066067488910522

21,3eec44be-b9e2-45a2-b919-05028f5a0ba9,0.07079585677115756,0.16920818686920963

22,9f836847-2b67-4b33-930a-1f84452628ba,0.07078522829778934,0.16919781903167638

23,fbaa8958-a5d5-4dfb-91f7-8c11afe226a8,0.07128542860765898,0.16834798505762455

24,a84b59c4-4145-472d-a26a-4c930648c16c,0.07196635776157265,0.17047633495883885

25,29cf8ad3-7068-4207-b0a2-4f0cff337c9f,0.0719701195278871,0.17051442269732875

26,d0f512c8-5c4f-427a-99e1-ebb4c5b363e5,0.0718787509597688,0.17054903897593635

27,74b1db2d-002b-4f89-8d02-ac084e9a3cd5,0.07089130417373782,0.16981103290127117

28,89210a0c-8144-491d-9e98-19e7f4c3085e,0.07076060461092577,0.1707011426749184

29,aebb377e-7c26-4bb5-8563-c3055a027844,0.07103977816965212,0.17113978347674103

30,00b527a0-d40a-44b4-90f9-750fd447d2d7,0.07097785505134419,0.16963542019904118

31,8c186559-f50d-40ca-a821-11596e1e5261,0.06992637446216321,0.17110063865050085

32,0e64cf14-6ccd-4ad0-9715-ab410f6baf6a,0.0718311255786932,0.1705675237580442

33,f5479823-1efe-47b8-9977-73dc41d1d69e,0.07016981880399553,0.1703708437681898

34,385cfa13-2476-4e3d-b755-3063a7f802b9,0.07016550435008462,0.17037054473511137

35,a40bf573-b701-46f0-9a06-5857cf3ab199,0.0701443567773146,0.17035314147536326

36,0c5a9751-2c1b-4003-834d-9584d2f907a2,0.07016050805421256,0.17038992836178396

37,65b09067-9cf0-492d-8a70-13d4f92f8a10,0.07137336818557355,0.1684713798357405

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

I have a dataframe that consists of two decimal values and an Id:

enter image description here

When I apply the as matrix function on the x and y values it yields an array that looks like this:

coords = df.as_matrix(columns=['x', 'y'])

coords

yields:

array([[ 0.0703843 ,  0.170845  ],

       [ 0.07022078,  0.17150128],

       [ 0.07208886,  0.17159163],

       ..., 

       [ 0.07162819,  0.17044404],

       [ 0.06951432,  0.17096308],

       [ 0.07104143,  0.17040137]])

This immediately seemed strange since the length of the decimal place were inconsistent but I just assumed pandas was doing some shortening for display purposes

But then when I tried to retrieve the IDs - I could only get one or zero matchs when they should all match:

ids = 

for coord in coords:

        try:

            _id = df.loc[df['x'] == coord[0]]['id'][1]

            ids.append(_id)

        except:                

            pass

len(ids)

1

What I am trying to understand is why the pd.as_matrix function extracts a value from the data frame that cannot be referenced again, and if so how do retrieve the ids from the data frame.

Any help here would be appreciated.

Thanks

Edit

Bellow is an subset of the data frame in CSV:

,id,x,y

0,07379a26-2447-4fce-83ac-4784abf07389,0.07038429591623253,0.17084500318384327

1,f5cc3adb-0588-4705-b1a3-fe1b669b776f,0.07022078416348305,0.17150127781674332

2,b5a57ffe-8565-4443-9685-11675ce25dc4,0.07208886125821728,0.17159163002146055

3,940efcaa-6d9d-4b10-a0fe-d8ec8c1d9c7e,0.07057468050347501,0.1700482708522834

4,616d7794-565a-4d2d-98cb-334beb5b91ef,0.07057895306948389,0.170054305037284

5,e2d1819d-1f58-407d-9950-be0a0c00374b,0.07161607658023798,0.17013089473907284

6,6a739687-f9ad-47bd-8a4b-c47bc4b2aec6,0.070163429153604,0.16889764101717875

7,dd2df646-9a66-4baa-8815-d24f1858eda7,0.07035099968831582,0.16995622800529742

8,6a224d76-efea-4313-803d-c25b619dae0a,0.07066777462044714,0.17021849979554743

9,321147fa-ee51-4bab-9634-199c92a42d2f,0.06984869509314469,0.17098101436534555

10,e52d6289-01ba-4e7d-8054-bb9a349c0505,0.07068704829137691,0.17029718331066224

11,517f256b-6171-4d93-9b4b-0f81aac828fb,0.0713283119291569,0.16983952831019206

12,e339c742-9784-49fc-a435-790db0364229,0.07131341496221469,0.1698513011377732

13,6f20ad5a-22fb-43a2-8885-838e5161df14,0.06942397329210678,0.1716572235671854

14,f6e1008f-2b22-4d88-8c84-c0dc4f2d822e,0.06942427697939664,0.17165098925109726

15,8a2d35e5-10a2-4188-b98f-54200d2db8da,0.07048162129308791,0.16896051533992895

16,adab8fd8-4348-412d-85d2-01491886967b,0.07076495746208027,0.16966622176968035

17,df79523b-848b-45a9-8dab-fe53c2a5b62d,0.06988926585338372,0.17028143287771583

18,db05d97c-3b16-4da8-9659-820fc7e3f858,0.0713167479593096,0.1685149810693375

19,d43963d1-b803-473c-85dc-2ed2e9f77f4e,0.07045583812582461,0.1706502407290604

20,9d99c9a6-2de3-4e6a-9bd7-9d7ece358a2f,0.07044174575566758,0.17066067488910522

21,3eec44be-b9e2-45a2-b919-05028f5a0ba9,0.07079585677115756,0.16920818686920963

22,9f836847-2b67-4b33-930a-1f84452628ba,0.07078522829778934,0.16919781903167638

23,fbaa8958-a5d5-4dfb-91f7-8c11afe226a8,0.07128542860765898,0.16834798505762455

24,a84b59c4-4145-472d-a26a-4c930648c16c,0.07196635776157265,0.17047633495883885

25,29cf8ad3-7068-4207-b0a2-4f0cff337c9f,0.0719701195278871,0.17051442269732875

26,d0f512c8-5c4f-427a-99e1-ebb4c5b363e5,0.0718787509597688,0.17054903897593635

27,74b1db2d-002b-4f89-8d02-ac084e9a3cd5,0.07089130417373782,0.16981103290127117

28,89210a0c-8144-491d-9e98-19e7f4c3085e,0.07076060461092577,0.1707011426749184

29,aebb377e-7c26-4bb5-8563-c3055a027844,0.07103977816965212,0.17113978347674103

30,00b527a0-d40a-44b4-90f9-750fd447d2d7,0.07097785505134419,0.16963542019904118

31,8c186559-f50d-40ca-a821-11596e1e5261,0.06992637446216321,0.17110063865050085

32,0e64cf14-6ccd-4ad0-9715-ab410f6baf6a,0.0718311255786932,0.1705675237580442

33,f5479823-1efe-47b8-9977-73dc41d1d69e,0.07016981880399553,0.1703708437681898

34,385cfa13-2476-4e3d-b755-3063a7f802b9,0.07016550435008462,0.17037054473511137

35,a40bf573-b701-46f0-9a06-5857cf3ab199,0.0701443567773146,0.17035314147536326

36,0c5a9751-2c1b-4003-834d-9584d2f907a2,0.07016050805421256,0.17038992836178396

37,65b09067-9cf0-492d-8a70-13d4f92f8a10,0.07137336818557355,0.1684713798357405

python pandas numpy dataframe

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

edited Nov 7 at 12:00

asked Nov 7 at 10:14

Johny Mudly

137

asked Nov 7 at 10:14

Johny Mudly

137

asked Nov 7 at 10:14

Johny Mudly

137

Also why are you using a bare except clause? What error is occoruing that you are ignoring?
– FHTMitchell
Nov 7 at 10:23

I just ran similar code (with manually copied dataframe and no try/except) and got no issues, every value matched. This sounds like a floating point rounding error. Try np.isclose rather than ==.
– FHTMitchell
Nov 7 at 10:27

@FHTMitchell Hi. The dataframe is not trivial to generate. I have included a subset in CSV. The error code is a key value error because the loc is returning an empty frame.
– Johny Mudly
Nov 7 at 12:02

@FHTMitchell Using a try except to see how many actually match.
– Johny Mudly
Nov 7 at 12:04

@FHTMitchell - You cant use np.isclose() in the df.loc function - so there is no way to locate it.
– Johny Mudly
Nov 7 at 12:32

add a comment |

Also why are you using a bare except clause? What error is occoruing that you are ignoring?
– FHTMitchell
Nov 7 at 10:23

I just ran similar code (with manually copied dataframe and no try/except) and got no issues, every value matched. This sounds like a floating point rounding error. Try np.isclose rather than ==.
– FHTMitchell
Nov 7 at 10:27

@FHTMitchell Hi. The dataframe is not trivial to generate. I have included a subset in CSV. The error code is a key value error because the loc is returning an empty frame.
– Johny Mudly
Nov 7 at 12:02

@FHTMitchell Using a try except to see how many actually match.
– Johny Mudly
Nov 7 at 12:04

@FHTMitchell - You cant use np.isclose() in the df.loc function - so there is no way to locate it.
– Johny Mudly
Nov 7 at 12:32

Also why are you using a bare except clause? What error is occoruing that you are ignoring?
– FHTMitchell
Nov 7 at 10:23

I just ran similar code (with manually copied dataframe and no try/except) and got no issues, every value matched. This sounds like a floating point rounding error. Try np.isclose rather than ==.
– FHTMitchell
Nov 7 at 10:27

@FHTMitchell Hi. The dataframe is not trivial to generate. I have included a subset in CSV. The error code is a key value error because the loc is returning an empty frame.
– Johny Mudly
Nov 7 at 12:02

@FHTMitchell Using a try except to see how many actually match.
– Johny Mudly
Nov 7 at 12:04

@FHTMitchell - You cant use np.isclose() in the df.loc function - so there is no way to locate it.
– Johny Mudly
Nov 7 at 12:32

add a comment |

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

The issue is with the df.loc function on geo-dataframes.

Once I exported it to a csv, then re read the dataframe in using normal pandas it seemed to work just fine.

Just letting who finds this know.

answered Nov 7 at 12:55

Johny Mudly

137

add a comment |

Your Answer

StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});

}
});

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53187390%2fwhy-does-pd-as-matrix-change-the-values-and-the-number-of-decimal-places-from%23new-answer', 'question_page');
}
);

Post as a guest

Name

Required, but never shown

1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

The issue is with the df.loc function on geo-dataframes.

Once I exported it to a csv, then re read the dataframe in using normal pandas it seemed to work just fine.

Just letting who finds this know.

answered Nov 7 at 12:55

Johny Mudly

137

add a comment |

up vote
0
down vote

accepted

The issue is with the df.loc function on geo-dataframes.

Once I exported it to a csv, then re read the dataframe in using normal pandas it seemed to work just fine.

Just letting who finds this know.

answered Nov 7 at 12:55

Johny Mudly

137

add a comment |

up vote
0
down vote

accepted

The issue is with the df.loc function on geo-dataframes.

Once I exported it to a csv, then re read the dataframe in using normal pandas it seemed to work just fine.

Just letting who finds this know.

answered Nov 7 at 12:55

Johny Mudly

137

The issue is with the df.loc function on geo-dataframes.

Once I exported it to a csv, then re read the dataframe in using normal pandas it seemed to work just fine.

Just letting who finds this know.

answered Nov 7 at 12:55

Johny Mudly

137

answered Nov 7 at 12:55

Johny Mudly

137

answered Nov 7 at 12:55

Johny Mudly

137

answered Nov 7 at 12:55

Johny Mudly

137

add a comment |

draft saved

draft discarded

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Name

Required, but never shown

Name

Required, but never shown

This page is only for reference, If you need detailed information, please check here

搜尋此網誌

Wsrtjtyk