{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"%matplotlib inline\nimport numpy as np\nimport pandas as pd \nfrom subprocess import check_output\n#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\nimport datetime"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"import seaborn as sns\nimport matplotlib.pyplot as plt"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"train = pd.read_csv(\"../input/train.csv\", parse_dates=['date_time'], nrows=10000000)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"train.info()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"mask = train.is_booking == True"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"trainm = train[mask]"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"#understanding the numerical content \n\nb1 = trainm[['srch_adults_cnt','srch_children_cnt','srch_rm_cnt']]"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"b1.describe()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"b1.info()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# putting the above separately\nsns.countplot(y='srch_adults_cnt', data=trainm)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# putting the children\nsns.countplot(y='srch_children_cnt', data=trainm)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# putting the children\nsns.countplot(y='srch_rm_cnt', data=trainm)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# putting the above separately\nsns.countplot(y='srch_adults_cnt', hue='srch_rm_cnt', data=trainm)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# putting the above separately\nsns.countplot(y='srch_adults_cnt', hue='srch_children_cnt', data=trainm)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"sns.set(style=\"ticks\", context=\"talk\")\n\n# Make a custom sequential palette using the cubehelix system\npal = sns.cubehelix_palette(4, 1.5, .75, light=.6, dark=.2)\n\n# Plot tip as a function of toal bill across days\ng = sns.lmplot(x=\"srch_children_cnt\", y=\"srch_adults_cnt\", hue=\"srch_rm_cnt\", data=trainm,\n               palette=pal, size=7)\n\n# Use more informative axis labels than are provided by default\ng.set_axis_labels(\"Number of Children\", \"Number of Adults\")"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}