dataframe

问题 I think this is very easy but I have no idea how to do it (or how to search it). Okay so I got 378 observations for a lot of countries. It goes like this ISO3C Date Country Vaccines Tests Confirmed Recovered Death Hospitalized Ventilation ICU 1 AFG 2020-01-01 Afghanistan 0 0 0 0 0 0 0 0 2 AFG 2020-01-02 Afghanistan 0 0 0 0 0 0 0 0 3 AFG 2020-01-03 Afghanistan 0 0 0 0 0 0 0 0 4 AFG 2020-01-04 Afghanistan 0 0 0 0 0 0 0 0 5 AFG 2020-01-05 Afghanistan 0 0 0 0 0 0 0 0 So How do I set a counter

问题 This question already has answers here : Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes (3 answers) Closed 6 months ago . I have two df columns with string values: df['starttime'] df['endtime'] 0 2015-10-06 18:35:33 0 2015-10-06 18:35:58 1 2015-10-08 17:51:21.999000 1 2015-10-08 17:52:10 2 2015-10-08 20:51:55.999000 2 2015-10-08 20:52:21 3 2015-10-05 15:16:49.999000 3 2015-10-05 15:17:00 4 2015-10-05 15:16:53.999000 4 2015-10-05 15:17:22 5 2015-10-05 15:17

问题 This question already has answers here : Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes (3 answers) Closed 6 months ago . I have two df columns with string values: df['starttime'] df['endtime'] 0 2015-10-06 18:35:33 0 2015-10-06 18:35:58 1 2015-10-08 17:51:21.999000 1 2015-10-08 17:52:10 2 2015-10-08 20:51:55.999000 2 2015-10-08 20:52:21 3 2015-10-05 15:16:49.999000 3 2015-10-05 15:17:00 4 2015-10-05 15:16:53.999000 4 2015-10-05 15:17:22 5 2015-10-05 15:17

问题 This question already has answers here : Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes (3 answers) Closed 6 months ago . I have two df columns with string values: df['starttime'] df['endtime'] 0 2015-10-06 18:35:33 0 2015-10-06 18:35:58 1 2015-10-08 17:51:21.999000 1 2015-10-08 17:52:10 2 2015-10-08 20:51:55.999000 2 2015-10-08 20:52:21 3 2015-10-05 15:16:49.999000 3 2015-10-05 15:17:00 4 2015-10-05 15:16:53.999000 4 2015-10-05 15:17:22 5 2015-10-05 15:17

问题 I'm reading a fixed width format (full source file) full of missing data, so pandas.read_fwf comes in handy. There is an empty line after the header, so I'm passing skip_blank_lines=True , but this appears to have no effect, as the first entry is still full of NaN/NaT: import io import pandas s="""USAF WBAN STATION NAME CTRY ST CALL LAT LON ELEV(M) BEGIN END 007018 99999 WXPOD 7018 +00.000 +000.000 +7018.0 20110309 20130730 007026 99999 WXPOD 7026 AF +00.000 +000.000 +7026.0 20120713 20170822

问题 I need to merge two dataframes without creating duplicate columns. The first datframe (dfa) has missing values. The second dataframe (dfb) has unique values. This would be the same as a vlookup in Excel. dfa looks like this: postcode lat lon ...plus 32 more columns M20 2.3 0.2 LS1 NaN NaN LS1 NaN NaN LS2 NaN NaN M21 2.4 0.3 dfb only contains unique Postcodes and values where lat and lon were NaN in dfa. It looks like this: postcode lat lon LS1 1.4 0.1 LS2 1.5 0.2 The output I would like is:

问题 I need to merge two dataframes without creating duplicate columns. The first datframe (dfa) has missing values. The second dataframe (dfb) has unique values. This would be the same as a vlookup in Excel. dfa looks like this: postcode lat lon ...plus 32 more columns M20 2.3 0.2 LS1 NaN NaN LS1 NaN NaN LS2 NaN NaN M21 2.4 0.3 dfb only contains unique Postcodes and values where lat and lon were NaN in dfa. It looks like this: postcode lat lon LS1 1.4 0.1 LS2 1.5 0.2 The output I would like is:

问题 I have a correlation matrix like so a b c a 1 0.5 0.3 b 0.5 1 0.7 c 0.3 0.7 1 And I want to transform this into a dataframe where the columns are like this: Letter1 letter2 correlation a a 1 a b 0.5 a c 0.3 b a 0.5 b b 1 . . . . . . Is there a pandas command to allow me to do this? Thanks in advance And a follow up to this, can I assign a value to the letters in Letter1 like so: Value1 Letter1 Value2 letter2 correlation 1 a 1 a 1 1 a 2 b 0.5 1 a 3 c 0.3 2 b 1 a 0.5 2 b 2 b 1 . . . . . . . . .

问题 I have a correlation matrix like so a b c a 1 0.5 0.3 b 0.5 1 0.7 c 0.3 0.7 1 And I want to transform this into a dataframe where the columns are like this: Letter1 letter2 correlation a a 1 a b 0.5 a c 0.3 b a 0.5 b b 1 . . . . . . Is there a pandas command to allow me to do this? Thanks in advance And a follow up to this, can I assign a value to the letters in Letter1 like so: Value1 Letter1 Value2 letter2 correlation 1 a 1 a 1 1 a 2 b 0.5 1 a 3 c 0.3 2 b 1 a 0.5 2 b 2 b 1 . . . . . . . . .

问题 I have a data.frame df where the column x is populated with integers (1-9). I would like to update columns y and z based on the value of x as follows: if x is 1,2, or 3 | y = 1 ## if x is 1,4, or 7 | z = 1 if x is 4,5, or 6 | y = 2 ## if x is 2,5, or 8 | z = 2 if x is 7,8, or 9 | y = 3 ## if x is 3,6, or 9 | z = 3 Below is a data.frame with the desired output for y and z df <- structure(list(x = c(1L, 2L, 3L, 3L, 4L, 2L, 1L, 2L, 5L, 2L, 1L, 6L, 3L, 7L, 3L, 2L, 1L, 4L, 3L, 2L), y = c(1L, 1L,