Merge CSV Files in Order by Date

When you merge Jan.csv, Feb.csv, and Mar.csv, you usually append them: Jan rows... then Feb rows... then Mar rows.

But what if your files aren't named nicely? Or what if File1.csv contains data from December and File2.csv contains data from January?

If you just append them, your timeline jumps back and forth. You need to Merge then Sort.

The Problem with "Append Only"

If you append blindly: Row 1: 2024-01-01 Row 2: 2024-01-02 ... Row 500: 2023-12-01 (Wait, we went back in time?)

This breaks charts, running totals, and time-series analysis.

Method 1: Excel

1. Merge the files (Copy/Paste or Power Query).
2. Select All data.
3. Data > Sort.
4. Choose your Date Column.
5. Order: Oldest to Newest.

Crucial Step: Ensure Excel recognizes the column as "Date" and not "Text". If it sorts like 01/01/2024, 01/02/2023, 02/01/2024, it's treating them as text.

Method 2: Python (Pandas)

Pandas can merge and sort in one go.

import pandas as pd
import glob

# Load all files
files = glob.glob("*.csv")
df_list = [pd.read_csv(f) for f in files]

# Merge
master_df = pd.concat(df_list)

# Convert column to datetime objects (crucial for sorting)
master_df['Date'] = pd.to_datetime(master_df['Date'])

# Sort
master_df = master_df.sort_values(by='Date')

# Save
master_df.to_csv("sorted_master.csv", index=False)

Method 3: Command Line (sort)

If your date format is ISO (YYYY-MM-DD), you can sort alphabetically because ISO dates sort correctly as text.

cat *.csv > combined.csv
sort -t, -k1 combined.csv > sorted.csv

Assumes Date is Column 1.

Warning: This sorts the Header row into the middle of the file! You need to handle the header separately.

Summary

Merging isn't enough. You must Sort after merging to ensure data integrity.

• ISO Dates (YYYY-MM-DD) are easiest to sort.
• US/UK Dates (MM/DD/YYYY) require a tool that understands date logic (like Excel or Pandas).

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Messy timeline? HappyCSV can merge your files and let you sort by any column before downloading.