Question: Python programming in Jupyter m_tom _adm _el _ch f_te f_ad fel 0 0 0 f_tof 0 10 2. 228 149 country_lyear m_in AD 2002 2002

Python programming in Jupyter

Python programming in Jupyter m_tom _adm _el _ch f_te f_ad fel 0

0 0 f_tof 0 10 2. 228 149 country_lyear m_in AD 2002

m_tom _adm _el _ch f_te f_ad fel 0 0 0 f_tof 0 10 2. 228 149 country_lyear m_in AD 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 52 0 2 2 0 0 19 _ch m_tem 1 4 183 149 0 0 2 1 14 130 131 2 1003 912 594 402 0 5 129 0 24 19 3 4 0 14 131 2 912 16 21 19 45 19 45 152 o 63 0 247 186 97 999 278 0 482 419 1 312 368 789 194 330 789 34 121 1 34 0 2 2. 5) Reshape the dataset in medical_data.csv to tidy format. Refer to below description and sample output. Name your final data frame medical_data_reshaped, it should have 120 rows, 5 columns. Data Description: country : Code for country year: Year data was collected . m_in: Male Infant m_to: Male Toddler mch: Male Child .m_te: Male Teen . m_ad: Male Adult . m_el: Male Elderly f_in: Female Infant f_to: Female Toddler f_ch: Female Child f_te: Female Teen f_ad: Female Adult f_el: Female Elderly Output: medical_data_reshaped.head(): 0 1 2 3 4 country year cases gender agegroup AD 20020 male infant AE 2002 2 male infant AF 2002 52 male infant AG 2002 O male intant AL 2002 2 male infant m_tom _adm _el _ch f_te f_ad fel 0 0 0 f_tof 0 10 2. 228 149 country_lyear m_in AD 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 52 0 2 2 0 0 19 _ch m_tem 1 4 183 149 0 0 2 1 14 130 131 2 1003 912 594 402 0 5 129 0 24 19 3 4 0 14 131 2 912 16 21 19 45 19 45 152 o 63 0 247 186 97 999 278 0 482 419 1 312 368 789 194 330 789 34 121 1 34 0 2 2. 5) Reshape the dataset in medical_data.csv to tidy format. Refer to below description and sample output. Name your final data frame medical_data_reshaped, it should have 120 rows, 5 columns. Data Description: country : Code for country year: Year data was collected . m_in: Male Infant m_to: Male Toddler mch: Male Child .m_te: Male Teen . m_ad: Male Adult . m_el: Male Elderly f_in: Female Infant f_to: Female Toddler f_ch: Female Child f_te: Female Teen f_ad: Female Adult f_el: Female Elderly Output: medical_data_reshaped.head(): 0 1 2 3 4 country year cases gender agegroup AD 20020 male infant AE 2002 2 male infant AF 2002 52 male infant AG 2002 O male intant AL 2002 2 male infant

Step by Step Solution

There are 3 Steps involved in it

1 Expert Approved Answer
Step: 1 Unlock blur-text-image
Question Has Been Solved by an Expert!

Get step-by-step solutions from verified subject matter experts

Step: 2 Unlock
Step: 3 Unlock

Students Have Also Explored These Related Databases Questions!