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| from datetime import datetime | |
| import pandas as pd | |
| def get_table_download_link(df: pd.DataFrame) -> str: | |
| """Generates a link for download the `df` locally as a csv file. | |
| Args: | |
| df: the dataframe to download | |
| Returns: | |
| href link | |
| """ | |
| import base64 | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() | |
| return f'<a href="data:file/csv;base64,{b64}" download="data_{datetime.now()}.csv">download</a>' | |
| def filter_df(df: pd.DataFrame, filter_: str) -> pd.DataFrame: | |
| """Takes a dataframe and a `filter_` keyword, returns all the rows that contain the value `filter_` in any column | |
| Args: | |
| df: pandas dataframe | |
| filter_: the string to search in the dataframe | |
| Returns: | |
| filtered dataframe | |
| """ | |
| import numpy as np | |
| mask = np.column_stack( | |
| [df[col].astype(str).str.contains(filter_, na=False) for col in df] | |
| ) | |
| filtered_df = df.loc[mask.any(axis=1)] | |
| return filtered_df | |