| import os |
| import pickle |
| import sys |
| from datetime import datetime |
|
|
| import pandas as pd |
| import numpy as np |
| import yaml |
|
|
| """A one liner to serialize df's into h5 files """ |
| def save_hdf(df, path): |
|
|
| if ".pickle" in path: |
| print(f"pickle in {path}!") |
| path = path.replace(".pickle","") |
|
|
| if ".h5" not in path: |
| path = path + ".h5" |
|
|
| if len(df) == 0: |
| print("not saving b/c data frame is empty") |
| else: |
| |
| date_cols = [x for x in df.columns if "Date" in x] |
| for date_col in date_cols: |
| |
| if df[date_col].dtype == np.object: |
| df[date_col] = pd.to_datetime(df[date_col]) |
| df = df.rename(columns = {date_col : date_col+"_date_object"}) |
| df.to_hdf(path_or_buf = path, key = 'df', format="table", mode = 'w') |
|
|
|
|
| def open_hdf(path): |
| if ".pickle" in path: |
| print(f"pickle in {path}!") |
| path = path.replace(".pickle","") |
|
|
| if ".h5" not in path: |
| path = path + ".h5" |
|
|
| if os.path.getsize(path)==0: |
| print("invalid file: size = 0") |
| return pd.DataFrame() |
|
|
| df = pd.read_hdf(path_or_buf = path, key= 'df' , mode= "r") |
|
|
| |
| date_object_cols = [x for x in df.columns if "date_object" in x] |
| for date_object_col in date_object_cols: |
| df[date_object_col] = df[date_object_col].dt.date |
| df = df.rename(columns = {date_object_col:date_object_col.replace("_date_object","")}) |
|
|
| return df |
|
|
| """ |
| saves a dict or dataframe as a pickle |
| - obj: the python object |
| - path: file path |
| - df_bool whether or not the object is a dataframe |
| - test_override: if true, then during testing, it's ok to overwrite the previous pickle |
| """ |
| def save_pickle(obj, path, df_bool=True, test_override = False): |
| config_user = open_yaml("config_user.yaml") |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| if ".pickle" not in path: |
| path = path + ".pickle" |
|
|
| if df_bool == True: |
| assert len(list(obj.index)) == len(set(list(obj.index))) |
| with open(path, 'wb') as outfile: |
| pickle.dump(obj.to_dict(), outfile) |
| else: |
| outfile = open(path, 'wb') |
| pickle.dump(obj, outfile) |
| outfile.close() |
|
|
| def read_gz(path): |
| if ".gz" not in path: |
| path = path + ".gz" |
| df = pd.read_csv(path, compression='gzip') |
| date_cols = [x for x in df.columns if "Date" in x] |
| for date_col in date_cols: |
| try: |
| df[date_col] = pd.to_datetime(df[date_col], infer_datetime_format=True) |
| except: |
| print(f"couldn't parse datetime of {date_col}") |
| return df |
|
|
| """A one liner to un-pickle df's and other stuff""" |
|
|
| def open_pickle(path, df_bool=True): |
| if ".pickle" not in path: |
| path = path + ".pickle" |
|
|
| start = datetime.now() |
| |
| infile = open(path, 'rb') |
| obj = pickle.load(infile) |
| infile.close() |
| end = datetime.now() |
| diff = end-start |
| |
|
|
| if df_bool == True: |
| return pd.DataFrame(obj) |
| else: |
| return obj |
|
|
|
|
| """ Opens a df (pickled or not) if it's in path, else it creates an empty dataframe with the specified col_names""" |
|
|
| def soft_df_open(path, obj_type="pickle",cols=[]): |
| if ".pickle" not in path: |
| path = path + ".pickle" |
|
|
| if os.path.isfile(path) == True: |
| if obj_type == "pickle": |
| df = open_pickle(path, df_bool=True) |
| elif ".csv" in path: |
| df = pd.read_csv(path) |
| else: |
| df="" |
| print("Don't know what kind of file you want to read") |
| sys.exit() |
|
|
| else: |
| print(f"Could not find {path}") |
| df = pd.DataFrame(columns=cols) |
| return df |
|
|
| """ Opens a yaml file as a dictionary""" |
| def open_yaml(path): |
| with open(path, 'r') as stream: |
| dic = yaml.safe_load(stream) |
| return dic |
|
|
| def update_csv(df, path): |
| """ appends a csv with provided df, specified in path, or creates csv with df provided |
| assumes the index is in the column named 'index'""" |
| if os.path.exists(path): |
| df_norm = pd.read_csv(path) |
| df_norm_updated = df_norm.append(df).drop_duplicates(keep="last", subset='index') |
| assert len(df_norm.columns) == len(df_norm_updated.columns) |
| df_norm_updated.to_csv(path, |
| index=False) |
| else: |
| df.to_csv(os.path.join(path), index=False) |
|
|
