mirror of
https://github.com/aljazceru/satshkd-vercel.git
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beac172ee3
- Removed all references to HKD and satshkd from codebase - Updated README, package.json, documentation to focus on EUR/sats - Removed HKD-specific files (convert_btcticker.js, archive/hkd_historical) - Updated all titles from SatsHKD/HKDSAT to SatsEUR/EURSAT - Removed HKD image reference from sats.hbs - Updated Python scripts to use 'historical' instead of 'hkd_historical' - Added all 16 missing EU official languages with translations: * Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da) * Estonian (et), Finnish (fi), Greek (el), Hungarian (hu) * Irish (ga), Latvian (lv), Lithuanian (lt), Maltese (mt) * Romanian (ro), Slovak (sk), Slovenian (sl), Swedish (sv) - Now supports all 24 EU official languages
57 lines
2.8 KiB
Markdown
57 lines
2.8 KiB
Markdown
```
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import pandas as pd
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import json
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with open("historical") as f:
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json_data = json.load(f)
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data_dict = {d["date"]: d for d in json_data}
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df = pd.DataFrame(data_dict.values())
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df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
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df.set_index("date", inplace=True)
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min_date = df.index.min()
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max_date = df.index.max()
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all_dates = pd.date_range(start=min_date, end=max_date, freq="D")
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all_dates_df = pd.DataFrame(index=all_dates)
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merged_df = pd.merge(all_dates_df, df, how="left", left_index=True, right_index=True)
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missing_dates = merged_df[merged_df.isnull().any(axis=1)].index.strftime("%Y-%m-%d").tolist()
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print(f"Missing dates: {missing_dates}")
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```
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# Importing necessary libraries
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import pandas as pd # Pandas for data manipulation
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import json # JSON for reading JSON file
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# Read the JSON data from a local file
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with open("historical") as f:
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json_data = json.load(f) # Reading data from file
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# Convert the list of dictionaries to a dictionary
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data_dict = {d["date"]: d for d in json_data} # Creating a dictionary with date as key and dictionary as value
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# Load the dictionary into a Pandas DataFrame
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df = pd.DataFrame(data_dict.values()) # Converting dictionary values to DataFrame
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df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d") # Converting date column to datetime format
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df.set_index("date", inplace=True) # Setting index to date column
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# Create a new DataFrame with all dates between the earliest and latest dates in the original DataFrame
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min_date = df.index.min() # Getting minimum date from index
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max_date = df.index.max() # Getting maximum date from index
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all_dates = pd.date_range(start=min_date, end=max_date, freq="D") # Creating range of all dates
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all_dates_df = pd.DataFrame(index=all_dates) # Creating DataFrame with all dates as index
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# Merge the original DataFrame with the new DataFrame containing all dates to find any missing dates
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merged_df = pd.merge(all_dates_df, df, how="left", left_index=True, right_index=True) # Merging DataFrames to get missing dates
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# Print any missing dates in the dataset
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missing_dates = merged_df[merged_df.isnull().any(axis=1)].index.strftime("%Y-%m-%d").tolist() # Filtering missing dates from merged DataFrame
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print(f"Missing dates: {missing_dates}") # Printing list of missing dates
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The code imports necessary libraries - Pandas and JSON, reads JSON data from a local file, creates a dictionary with date as key and dictionary as value, converts the dictionary to a Pandas DataFrame, sets the index of the DataFrame to the date column, creates a new DataFrame with all dates between the earliest and latest dates in the original DataFrame, merges the original DataFrame with the new DataFrame containing all dates to find any missing dates, filters missing dates from the merged DataFrame, and finally prints a list of the missing dates.
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