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In jupyter notebooks python with indentations 5) Reshape the dataset in medical_data.csv to tidy format. Refer to below description and sample output. Name your final

In jupyter notebooks python with indentations

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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 min: Male Infant m_to: Male Toddler m_ch: Male Child m_te: Male Teen m_ad: Male Adult mel: 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(): country year cases gender agegroup infant 1 infant AD 2002 AE 2002 AF 2002 AG 2002 AL 2002 O 2 52 O 2 male male male male male infant infant infant medical_data AD AE AF AG AL country year m_in m_to 2002 0 0 2002 2 4 2002 52 228 2002 0 0 2002 2 19 2002 2 152 2002 0 0 2002 186 999 2002 97 278 2002 0 0 m_ch m_tem_ad m_el f_in f_to f_ch f_te f_ad f_el 1 0 0 0 0 0 0 4 4 4 6 5 12 10 3 4 2 4 183 149 149 129 94 80 93 7 77 77 0 0 0 0 0 1 1 0 0 0 21 14 14 24 19 16 3 4 19 19 130 131 131 63 26 21 1 45 45 45 1 2 2 0 0 0 0 0 0 0 1003 912 912 482 312 194 247 89 789 789 594 402 402 419 368 330 121 32 34 34 0 0 0 1 1 0 0 2 2 2 AM AN AR AS 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 min: Male Infant m_to: Male Toddler m_ch: Male Child m_te: Male Teen m_ad: Male Adult mel: 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(): country year cases gender agegroup infant 1 infant AD 2002 AE 2002 AF 2002 AG 2002 AL 2002 O 2 52 O 2 male male male male male infant infant infant medical_data AD AE AF AG AL country year m_in m_to 2002 0 0 2002 2 4 2002 52 228 2002 0 0 2002 2 19 2002 2 152 2002 0 0 2002 186 999 2002 97 278 2002 0 0 m_ch m_tem_ad m_el f_in f_to f_ch f_te f_ad f_el 1 0 0 0 0 0 0 4 4 4 6 5 12 10 3 4 2 4 183 149 149 129 94 80 93 7 77 77 0 0 0 0 0 1 1 0 0 0 21 14 14 24 19 16 3 4 19 19 130 131 131 63 26 21 1 45 45 45 1 2 2 0 0 0 0 0 0 0 1003 912 912 482 312 194 247 89 789 789 594 402 402 419 368 330 121 32 34 34 0 0 0 1 1 0 0 2 2 2 AM AN AR AS

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