def preprocess(df): """Preprocess the dataframe - Appends the index to the dataframe when it contains information - Rename the "index" column to "df_index", if exists - Convert the DataFrame's columns to str Args: df: the pandas DataFrame Returns: The preprocessed DataFrame """ # Treat index as any other column if ( not pd.Index(np.arange(0, len(df))).equals(df.index) or df.index.dtype != np.int64 ): df = df.reset_index() # Rename reserved column names df = rename_index(df) # Ensure that columns are strings df.columns = df.columns.astype("str") return df
def __init__(self, df, config_file: Path = None, **kwargs): if config_file: config.config.set_file(str(config_file)) config.set_kwargs(kwargs) # Treat index as any other column if (not pd.Index(np.arange(0, len(df))).equals(df.index) or df.index.dtype != np.int64): df = df.reset_index() # Rename reserved column names df = rename_index(df) # Remove spaces and colons from column names df = clean_column_names(df) # Sort column names sort = config["sort"].get(str) if sys.version_info[1] <= 5 and sort != "None": warnings.warn("Sorting is supported from Python 3.6+") if sort in ["asc", "ascending"]: df = df.reindex(sorted(df.columns, key=lambda s: s.casefold()), axis=1) elif sort in ["desc", "descending"]: df = df.reindex(reversed( sorted(df.columns, key=lambda s: s.casefold())), axis=1) elif sort != "None": raise ValueError( '"sort" should be "ascending", "descending" or None.') # Store column order config["column_order"] = df.columns.tolist() # Get dataset statistics description_set = describe_df(df) # Get sample sample = {} n_head = config["samples"]["head"].get(int) if n_head > 0: sample["head"] = df.head(n=n_head) n_tail = config["samples"]["tail"].get(int) if n_tail > 0: sample["tail"] = df.tail(n=n_tail) # Render HTML self.html = to_html(sample, description_set) self.minify_html = config["minify_html"].get(bool) self.use_local_assets = config["use_local_assets"].get(bool) self.title = config["title"].get(str) self.description_set = description_set self.sample = sample
def preprocess(df): # Treat index as any other column if (not pd.Index(np.arange(0, len(df))).equals(df.index) or df.index.dtype != np.int64): df = df.reset_index() # Rename reserved column names df = rename_index(df) # Ensure that columns are strings df.columns = df.columns.astype("str") return df
def __init__(self, df, minimal=False, config_file: Path = None, **kwargs): if sys.version_info <= (3, 5): warnings.warn( "This is the last release to support Python 3.5, please upgrade.", category=DeprecationWarning, ) if config_file is not None and minimal: raise ValueError( "Arguments `config_file` and `minimal` are mutually exclusive." ) if minimal: config_file = get_config_minimal() if config_file: config.set_file(str(config_file)) config.set_kwargs(kwargs) self.date_start = datetime.utcnow() # Treat index as any other column if (not pd.Index(np.arange(0, len(df))).equals(df.index) or df.index.dtype != np.int64): df = df.reset_index() # Rename reserved column names df = rename_index(df) # Ensure that columns are strings df.columns = df.columns.astype("str") # Get dataset statistics description_set = describe_df(df) # Build report structure self.sample = self.get_sample(df) self.title = config["title"].get(str) self.description_set = description_set self.date_end = datetime.utcnow() disable_progress_bar = not config["progress_bar"].get(bool) with tqdm(total=1, desc="build report structure", disable=disable_progress_bar) as pbar: self.report = get_report_structure(self.date_start, self.date_end, self.sample, description_set) pbar.update()
def __init__(self, df, minimal=False, config_file: Path = None, **kwargs): if config_file is not None and minimal: raise ValueError( "Arguments `config_file` and `minimal` are mutually exclusive." ) if minimal: config_file = get_config_minimal() if config_file: config.config.set_file(str(config_file)) config.set_kwargs(kwargs) self.date_start = datetime.utcnow() # Treat index as any other column if ( not pd.Index(np.arange(0, len(df))).equals(df.index) or df.index.dtype != np.int64 ): df = df.reset_index() # Rename reserved column names df = rename_index(df) # Ensure that columns are strings df.columns = df.columns.astype("str") # Sort names according to config (asc, desc, no sort) df = self.sort_column_names(df) config["column_order"] = df.columns.tolist() # Get dataset statistics description_set = describe_df(df) # Build report structure self.sample = self.get_sample(df) self.title = config["title"].get(str) self.description_set = description_set self.date_end = datetime.utcnow() self.report = get_report_structure( self.date_start, self.date_end, self.sample, description_set )