{"metadata":{"language_info":{"name":"python","version":"3.7.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Only use the first line if you are using a Jupyter Notebook\n%matplotlib inline \nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt","metadata":{"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"fd = pd.read_csv('feather.csv')\nhd = pd.read_csv('heltec.csv')","metadata":{"trusted":true},"execution_count":75,"outputs":[]},{"cell_type":"code","source":"fd.tail(1)","metadata":{"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":" timestamp deviceId bmp_temp bmp_press temp humid co2 mic\n999 1607278252 15 21.32 NaN 22.78 27.73 601 0.0","text/html":"
\n | timestamp | \ndeviceId | \nbmp_temp | \nbmp_press | \ntemp | \nhumid | \nco2 | \nmic | \n
---|---|---|---|---|---|---|---|---|
999 | \n1607278252 | \n15 | \n21.32 | \nNaN | \n22.78 | \n27.73 | \n601 | \n0.0 | \n