Revisions to Appending two dataframes with the same column name. No NaN values [duplicate]

added 145 characters in body

edited Dec 11, 2018 at 21:57

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 zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002500000

 zone_temp_1 Date/Time 15:36:20 73.500000 15:3638:40 73.500000 15:3739:00 73.400002 15:3739:20 73.400002 15:3739:40 73.400002 15:3840:00 73.400002

 zone_temp_1 Date/Time 15:36:20 temp 73.500000 1415:2136:0040  73.4500000 1415:2237:00 74 73.400002 1415:2337:0020  73.2400002 1415:2437:3040 72 73.4400002 1415:2538:00 74 73.400002 1415:2538:3020 71 73.2400002 15:39:20 73.500000 15:39:40 73.500000 15:40:00 73.400002

 0 zone_temp_1 Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:3638:2040 NaN 73.500000 15:3638:4020 NaN 73.500000 15:3739:00 NaN 73.400002 15:3739:20 NaN 73.400002 15:3739:40 NaN 73.400002 15:3840:00 NaN 73.400002

 zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

 zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

Date/Time temp 14:21:00 73.4 14:22:00 74 14:23:00 73.2 14:24:30 72.4 14:25:00 74 14:25:30 71.2

 0 zone_temp_1 Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

 zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.500000

 zone_temp_1 Date/Time 15:38:40 73.500000 15:39:00 73.400002 15:39:20 73.400002 15:39:40 73.400002 15:40:00 73.400002

 zone_temp_1 Date/Time 15:36:20  73.500000 15:36:40  73.500000 15:37:00  73.400002 15:37:20  73.400002 15:37:40  73.400002 15:38:00  73.400002 15:38:20  73.400002 15:39:20 73.500000 15:39:40 73.500000 15:40:00 73.400002

 0 zone_temp_1 Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:38:40 NaN 73.500000 15:38:20 NaN 73.500000 15:39:00 NaN 73.400002 15:39:20 NaN 73.400002 15:39:40 NaN 73.400002 15:40:00 NaN 73.400002

added 128 characters in body

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edited Dec 11, 2018 at 21:42

BENY

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  zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

  zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

  0 zone_temp_1 Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

 zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

 zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

 0 zone_temp_1

Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

  zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

  zone_temp_1 Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

  0 zone_temp_1 Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

added 485 characters in body

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edited Dec 11, 2018 at 21:40

KDMcCracken

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Two Dataframes:

df1:

Date/Time temp 14:21:00  73.4 14:22:00 74 14:23:00 73.2zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

df2:

Date/Time temp 14:24:30 72.4 14:25:00 74 14:25:30  71.2zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

I would like the concatenation to look like this:

Date/Time temp 14:21:00 73.4 14:22:00 74 14:23:00 73.2 14:24:30 72.4 14:25:00 74 14:25:30 71.2

Using pandas.concat as such df1= pd.concat([df1, df2])"df1= pd.concat([df1, df2])" leaves me with this

Date/Time 0  temp 14:21:00 73.4  NaN 14:22:00 74 NaN 14:23:00 73.2 NaN 14:24:30 NaN 72.4 14:25:00 NaN 74 14:25:30 NaN 0 71.2zone_temp_1

Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

This was marked as a repeat question but I have done my due diligence and can not find any solution.

Two Dataframes:

df1:

Date/Time temp 14:21:00  73.4 14:22:00 74 14:23:00 73.2

df2:

Date/Time temp 14:24:30 72.4 14:25:00 74 14:25:30  71.2

I would like the concatenation to look like this:

Date/Time temp 14:21:00 73.4 14:22:00 74 14:23:00 73.2 14:24:30 72.4 14:25:00 74 14:25:30 71.2

Using pandas.concat as such df1= pd.concat([df1, df2]) leaves me with this

Date/Time 0  temp 14:21:00 73.4  NaN 14:22:00 74 NaN 14:23:00 73.2 NaN 14:24:30 NaN 72.4 14:25:00 NaN 74 14:25:30 NaN  71.2

This was marked as a repeat question but I have done my due diligence and can not find any solution.

Two Dataframes:

df1:

 zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002 15:38:20 73.400002

df2:

 zone_temp_1

Date/Time 15:36:20 73.500000 15:36:40 73.500000 15:37:00 73.400002 15:37:20 73.400002 15:37:40 73.400002 15:38:00 73.400002

I would like the concatenation to look like this:

Date/Time temp 14:21:00 73.4 14:22:00 74 14:23:00 73.2 14:24:30 72.4 14:25:00 74 14:25:30 71.2

Using pandas.concat as such "df1= pd.concat([df1, df2])" leaves me with this

 0 zone_temp_1

Date/Time 15:36:20 73.500000 NaN 15:36:40 73.500000 NaN 15:37:00 73.400002 NaN 15:37:20 73.400002 NaN 15:37:40 73.400002 NaN 15:38:00 73.400002 NaN 15:38:20 73.400002 NaN 15:36:20 NaN 73.500000 15:36:40 NaN 73.500000 15:37:00 NaN 73.400002 15:37:20 NaN 73.400002 15:37:40 NaN 73.400002 15:38:00 NaN 73.400002

This was marked as a repeat question but I have done my due diligence and can not find any solution.