intro-data-science.ipynb

{"cells":[{"cell_type":"markdown","source":["# The basics"],"metadata":{}},{"cell_type":"code","source":["print('hello world')"],"metadata":{},"outputs":[],"execution_count":2},{"cell_type":"code","source":["breakfast_menu = [\"natural bridges tofu scramble\", \"eggs\", \"bacon\"]\nbreakfast_menu.append(\"bagels\")\nbreakfast_menu"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":["# Finding files"],"metadata":{}},{"cell_type":"code","source":["%sh ls"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["%sh ls /dbfs/databricks-datasets/COVID"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["%sh ls /dbfs/databricks-datasets/COVID/CSSEGISandData/csse_covid_19_data/csse_covid_19_daily_reports"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":["the `head` command displays the first few rows of the .csv file"],"metadata":{}},{"cell_type":"code","source":["%sh head /dbfs/databricks-datasets/COVID/CSSEGISandData/csse_covid_19_data/csse_covid_19_daily_reports/04-15-2020.csv"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"markdown","source":["# Starting to use the items"],"metadata":{}},{"cell_type":"code","source":["import pandas as pd"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["dataframe = pd.read_csv(\"/dbfs/databricks-datasets/COVID/CSSEGISandData/csse_covid_19_data/csse_covid_19_daily_reports/04-15-2020.csv\")"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"markdown","source":["Let's first drop the `FIPS` column and then rename `Admin2` to `City`"],"metadata":{}},{"cell_type":"code","source":["dataframe = dataframe.drop(\"FIPS\", axis=1)"],"metadata":{},"outputs":[],"execution_count":14},{"cell_type":"code","source":["dataframe.rename(columns={'Admin2':'City'}, inplace=True)"],"metadata":{},"outputs":[],"execution_count":15},{"cell_type":"markdown","source":["### What can the dataframe tell us?"],"metadata":{}},{"cell_type":"code","source":["dataframe.sum()"],"metadata":{},"outputs":[],"execution_count":17},{"cell_type":"code","source":["dataframe.mean()"],"metadata":{},"outputs":[],"execution_count":18},{"cell_type":"markdown","source":["### Lets start manipulating the data\nWhat do we have to play with?"],"metadata":{}},{"cell_type":"code","source":["dataframe.columns"],"metadata":{},"outputs":[],"execution_count":20},{"cell_type":"markdown","source":["Lets do some basic data manipulation"],"metadata":{}},{"cell_type":"code","source":["dataframe.sort_values(\"Confirmed\", ascending=False)"],"metadata":{},"outputs":[],"execution_count":22},{"cell_type":"code","source":["dataframe[dataframe[\"Country_Region\"] == \"US\"].sort_values(\"Confirmed\", ascending=False)"],"metadata":{},"outputs":[],"execution_count":23},{"cell_type":"code","source":["dataframe[dataframe[\"Country_Region\"] == \"US\"].groupby(\"Province_State\")[\"Confirmed\"].sum().sort_values(ascending=False)"],"metadata":{},"outputs":[],"execution_count":24},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":25}],"metadata":{"name":"intro-data-science","notebookId":3562984204546748},"nbformat":4,"nbformat_minor":0}