dataframe

问题 I am struggling with MultiIndex DataFrame in python pandas. Suppose I have a df like this: count day group name A Anna 10 Monday Beatrice 15 Tuesday B Beatrice 15 Wednesday Cecilia 20 Thursday What I need is to find the maximum in name for each group and remove it from the dataframe. The final df would look like: count day group name A Anna 10 Monday B Beatrice 15 Wednesday Does any of you have any idea how to do this? I am running out of ideas... Thanks in advance! EDIT: What if the original

问题 I am struggling with MultiIndex DataFrame in python pandas. Suppose I have a df like this: count day group name A Anna 10 Monday Beatrice 15 Tuesday B Beatrice 15 Wednesday Cecilia 20 Thursday What I need is to find the maximum in name for each group and remove it from the dataframe. The final df would look like: count day group name A Anna 10 Monday B Beatrice 15 Wednesday Does any of you have any idea how to do this? I am running out of ideas... Thanks in advance! EDIT: What if the original

问题 I am struggling with MultiIndex DataFrame in python pandas. Suppose I have a df like this: count day group name A Anna 10 Monday Beatrice 15 Tuesday B Beatrice 15 Wednesday Cecilia 20 Thursday What I need is to find the maximum in name for each group and remove it from the dataframe. The final df would look like: count day group name A Anna 10 Monday B Beatrice 15 Wednesday Does any of you have any idea how to do this? I am running out of ideas... Thanks in advance! EDIT: What if the original

问题 I have a data frame that has a 3 columns. Time represents every day of the month for various months. what I am trying to do is get the 'Count' value per day and average it per each month, and do this for each country. The output must be in the form of a data frame. Curent data: Time Country Count 2017-01-01 us 7827 2017-01-02 us 7748 2017-01-03 us 7653 .. .. 2017-01-30 us 5432 2017-01-31 us 2942 2017-01-01 us 5829 2017-01-02 ca 9843 2017-01-03 ca 7845 .. .. 2017-01-30 ca 8654 2017-01-31 ca

问题 I have a data frame that has a 3 columns. Time represents every day of the month for various months. what I am trying to do is get the 'Count' value per day and average it per each month, and do this for each country. The output must be in the form of a data frame. Curent data: Time Country Count 2017-01-01 us 7827 2017-01-02 us 7748 2017-01-03 us 7653 .. .. 2017-01-30 us 5432 2017-01-31 us 2942 2017-01-01 us 5829 2017-01-02 ca 9843 2017-01-03 ca 7845 .. .. 2017-01-30 ca 8654 2017-01-31 ca

问题 I have a data frame that has a 3 columns. Time represents every day of the month for various months. what I am trying to do is get the 'Count' value per day and average it per each month, and do this for each country. The output must be in the form of a data frame. Curent data: Time Country Count 2017-01-01 us 7827 2017-01-02 us 7748 2017-01-03 us 7653 .. .. 2017-01-30 us 5432 2017-01-31 us 2942 2017-01-01 us 5829 2017-01-02 ca 9843 2017-01-03 ca 7845 .. .. 2017-01-30 ca 8654 2017-01-31 ca

问题 I'm using Pandas 0.19. Considering the following data frame: FID admin0 admin1 admin2 windspeed population 0 cntry1 state1 city1 60km/h 700 1 cntry1 state1 city1 90km/h 210 2 cntry1 state1 city2 60km/h 100 3 cntry1 state2 city3 60km/h 70 4 cntry1 state2 city4 60km/h 180 5 cntry1 state2 city4 90km/h 370 6 cntry2 state3 city5 60km/h 890 7 cntry2 state3 city6 60km/h 120 8 cntry2 state3 city6 90km/h 420 9 cntry2 state3 city6 120km/h 360 10 cntry2 state4 city7 60km/h 740 How can I create a table

问题 I'm using Pandas 0.19. Considering the following data frame: FID admin0 admin1 admin2 windspeed population 0 cntry1 state1 city1 60km/h 700 1 cntry1 state1 city1 90km/h 210 2 cntry1 state1 city2 60km/h 100 3 cntry1 state2 city3 60km/h 70 4 cntry1 state2 city4 60km/h 180 5 cntry1 state2 city4 90km/h 370 6 cntry2 state3 city5 60km/h 890 7 cntry2 state3 city6 60km/h 120 8 cntry2 state3 city6 90km/h 420 9 cntry2 state3 city6 120km/h 360 10 cntry2 state4 city7 60km/h 740 How can I create a table

问题 I have a dataframe df and it looks like this: id Type agent_id created_at 0 44525 Stunning 6 bedroom villa in New Delhi 184 2018-03-09 1 44859 Villa for sale in Amritsar 182 2017-02-19 2 45465 House in Faridabad 154 2017-04-17 3 50685 5 Hectre land near New Delhi 113 2017-09-01 4 130728 Duplex in Mumbai 157 2017-02-07 5 130856 Large plot with fantastic views in Mumbai 137 2018-01-16 6 130857 Modern Design Penthouse in Bangalore 199 2017-03-24 I've this tabular data and I'm trying to clean

问题 I have a dataframe df and it looks like this: id Type agent_id created_at 0 44525 Stunning 6 bedroom villa in New Delhi 184 2018-03-09 1 44859 Villa for sale in Amritsar 182 2017-02-19 2 45465 House in Faridabad 154 2017-04-17 3 50685 5 Hectre land near New Delhi 113 2017-09-01 4 130728 Duplex in Mumbai 157 2017-02-07 5 130856 Large plot with fantastic views in Mumbai 137 2018-01-16 6 130857 Modern Design Penthouse in Bangalore 199 2017-03-24 I've this tabular data and I'm trying to clean