{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f238ae78c50>"]},"execution_count":1,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["%matplotlib inline\n","\n","import pandas\n","\n","data = pandas.read_csv(\"gapminder.csv\", index_col=\"country\")\n","\n","years = data.columns.str.strip(\"gdpPercap_\")  # Extract year from last 4 characters of each column name\n","data.columns = years.astype(int)              # Convert year values to integers, saving results back to dataframe\n","\n","data.loc[\"Australia\"].plot()"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.9"}},"nbformat":4,"nbformat_minor":5}