Got it, let's walk through how to compute the average Price per date using your TimeAndSale dataset. I'll assume you're using pandas (the standard tool for this kind of tabular/time-series data work) — here's a straightforward, step-by-step approach:

Step 1: Load and Prepare Your Data #

First, make sure your timestamp column (#=TimeAndSale) is properly parsed as a datetime type. If it's stored as plain text right now, convert it:

import pandas as pd

# Load your data (adjust the read method to match your source: CSV, Excel, etc.)
df = pd.read_csv("your_time_and_sale_data.csv")

# Convert the timestamp column to datetime
df["#=TimeAndSale"] = pd.to_datetime(df["#=TimeAndSale"])

Step 2: Extract Date from Timestamp #

Create a new column that isolates just the date part (we'll use this to group our data):

df["trade_date"] = df["#=TimeAndSale"].dt.date

Step 3: Group by Date and Calculate Average Price #

Use pandas' groupby() function to aggregate the Price values by date, then compute the mean:

# Calculate daily average price, reset index to make it a clean DataFrame
daily_avg_price = df.groupby("trade_date")["Price"].mean().reset_index()

# Optional: Round the average to 2 decimal places for readability
daily_avg_price["Price"] = daily_avg_price["Price"].round(2)

Notes to Keep in Mind #

• If your dataset has missing Price values, pandas' mean() will automatically skip them by default. If you want to change this behavior, use mean(skipna=False) (though this will return NaN for dates with any missing prices).
• If you need to filter by a specific EventSymbol first (e.g., only calculate averages for a particular stock), add a filter before grouping:

  filtered_df = df[df["EventSymbol"] == "YOUR_SYMBOL"]
  daily_avg_price = filtered_df.groupby("trade_date")["Price"].mean().reset_index()
  

That's it! The daily_avg_price DataFrame will have two columns: trade_date (the date) and Price (the average price for that day).

内容的提问来源于stack exchange，提问作者Jeremie