{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom matplotlib import pyplot as plt\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path = '../input/Kannada-MNIST/'\nfile_name_dict= {'dig':'Dig-MNIST','test':'test','train':'train','sample':'sample_submission'}\ndf_dict = {key:pd.read_csv(path + value + '.csv') for key, value in file_name_dict.items()}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def visualisation_pipe(df):\n    df = df.copy()\n    try:\n        df = df.drop('label', axis=1)\n    except:\n        df = df.drop('id', axis=1)\n        return df\n    return df\n\ndef preview_MNIST(df, n_rows=2, n_columns=5):\n    df = df.copy()\n    df_train_viz = df.pipe(visualisation_pipe)\n    fig, ax = plt.subplots(n_rows,n_columns,figsize = (n_columns*3,n_rows*2))\n    for row_index in range(0, n_rows*n_columns):\n        ax[row_index%n_rows,row_index//n_rows].imshow(df_train_viz.iloc[row_index].values.reshape(28,28),cmap='binary')\n    fig.suptitle('Preview of Kanada MNIST')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for key in ['test', 'train', 'dig']:\n    preview_MNIST(df_dict[key], 5, 5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}