Python_01i

Simple dataframe

First import the libraries to do the plots and conversions

(Which libraries you choose to use comes with experience)

import pandas as pd

import seaborn as sns

Then create some dummy data 

#dummy example data in the form of a 'dictionary'

table1 = {'names': ['Jack', 'Clara','Alex', \

                    'Julia', 'Steve', 'Suki',\

                    'Yoko', 'Julie', 'Stephan',\

                    'James', 'Carl','Amy', \

                    'Judith', 'Sam', 'Amita',\

                    'Aoi', 'Julia', 'Stephan'],

          'gender': ['M','F','F','F','M','F','F','F','M',\

                     'M','F','M','F','M','F','F','F','M'],

          'weight':[76, 51, 55, 59, 90, 57, 49, 58, 92,\

                    73, 54, 51, 61, 89, 58, 50, 55, 87],

          'height': [173, 168, 165, 171, 167, 158, 138, 178, 173, \

                     164, 173, 155, 168, 171, 151, 139, 172, 169]}

Change the dummy data to a DataFrame to suit the Seaborn plot 

Make a plot and Save the data for later to a generic csv file

patients = pd.DataFrame(table1)

sns.pairplot(patients, hue='gender')

patients.to_csv('patients_info.csv', index=False)

Now we can load and use the data and do some stats

p_data = pd.read_csv("patients_info.csv")

p_means = p_data.mean(numeric_only=True)

working_data = int(p_means.height)

x = p_data.iat[1,1]

y = p_data.at[5, 'weight']

z = p_data.get_value(5, 'names')