How to use time_series_schema method in pandera

Best Python code snippet using pandera_python

alpha_vantage_constants.py

Source:alpha_vantage_constants.py

1class AlphaVantageConstants:2    intra_day_function = "TIME_SERIES_INTRADAY"3    intra_day_extended_function = "TIME_SERIES_INTRADAY_EXTENDED"4    daily_function = "TIME_SERIES_DAILY"5    daily_adjusted_function = "TIME_SERIES_DAILY_ADJUSTED"6    weekly_function = "TIME_SERIES_WEEKLY"7    weekly_Adjusted_function = "TIME_SERIES_WEEKLY_ADJUSTED"8    monthly_function = "TIME_SERIES_MONTHLY"9    monthly_adjusted_function = "TIME_SERIES_MONTHLY_ADJUSTED"10    min_interval = lambda minute: "{}min".format(minute)11    slice_year_month = lambda yr, mh: "year{}month{}".format(yr, mh)12    meta_deta_schema = {13        "type": "object",14        "properties": {15            "Meta Data": {"type": "object"},16            "properties": {17                "1. Information": {"type": "string"},18                "2. Symbol": {"type": "string"},19                "3. Last Refreshed": {"type": "string"},20                "4. Interval": {"type": "string"},21                "5. Output Size": {"type": "string"},22                "6. Time Zone": {"type": "string"}23            }24        },25        "required": ["Meta Data"]26    }27    time_series_schema = {28        "type": "object",29        "properties": {30            "1. open": {"type": "string"},31            "2. high": {"type": "string"},32            "3. low": {"type": "string"},33            "4. close": {"type": "string"},34            "5. volume": {"type": "string"},35        },36        "required": ["1. open", "2. high", "3. low", "4. close", "5. volume"]37    }38    daily_series_adjusted_schema = {39        "type": "object",40        "properties": {41            "Information": {"type": "string"},42            },43        "required": ["Information"]44    }45    weekly_adjusted_time_series_schema = {46        "type": "object",47        "properties": {48            "1. open": {"type": "string"},49            "2. high": {"type": "string"},50            "3. low": {"type": "string"},51            "4. close": {"type": "string"},52            "5. adjusted close": {"type": "string"},53            "6. volume": {"type": "string"},54            "7. dividend amount": {"type": "string"},55        },56        "required": ["1. open", "2. high", "3. low", "4. close", "5. adjusted close", "6. volume", "7. dividend amount"]...

series_schema.py

Source:series_schema.py

...19                allow_duplicates=True,20                name="my_series")21        self.series = pd.Series(["foobar", "foobar", "foobar"],22                                name="my_series")23    def time_series_schema(self):24        self.schema.validate(self.series)25    def mem_series_schema(self):26         self.schema.validate(self.series)27    def peakmem_series_schema(self):...

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