This guide explains how to use the Float API to import log time entries from a CSV file. Use this process when you are migrating from another tool, syncing offline data, or moving from manual tracking.

📝 Note: You do not need to be a developer to follow this guide, but basic terminal familiarity helps. This article walks through installing Python, preparing your CSV, configuring the script, and running a dry run before sending data.

Step 1: Install Python

Python is a programming language we will use to run the import script.

Visit https://www.python.org/downloads/
Click the yellow Download Python button for your operating system.
Run the installer:
- Windows: During installation, make sure to check "Add Python to PATH".
- Mac: Use the .pkg installer provided.
Once installed, open your terminal:
- Windows: Open "Command Prompt" or "Windows Terminal"
- Mac: Use the built-in "Terminal" app
Verify Python is installed by running:
```
python3 --version
```
You should see a version number like Python 3.12.0

Step 2: Install required Python libraries

We need two libraries: pandas for reading CSV files and requests for sending data to the Float API.

In your terminal, run:

pip3 install pandas requests

Step 3: Prepare your CSV file

Create a CSV file with the following headers:

people_id,project_id,hours,date,task_name

Each row should represent a time entry, for example:

12345,67890,1.5,2025-07-09,Client Meeting 
23456,67890,0.75,2025-07-10,Design Review

Save the file as something like time_entries.csv.

Step 4: Get your Float API key

Log in to your team's Float account as the Account Owner. Only the Account Owner can access the API key.
Go to Team Settings > Integrations.
Copy the API Key.

Keep this key secret!

You can explore Float’s full API documentation at https://developer.float.com/.

Step 5: Copy and configure the script

Open any text editor (like Notepad, VS Code, or TextEdit) and paste the following script:

import pandas as pd
import requests
import time
import os

# === CONFIGURATION ===
CSV_FILE = "/mnt/data/testAPIscript.csv"
FLOAT_API_URL = "https://api.float.com/v3/logged-time"
BEARER_TOKEN = "your_float_api_key_here"  # ← Replace with your actual API key
DRY_RUN = True  # Set to False to actually send data to Float

REQUIRED_COLUMNS = {"people_id", "project_id", "hours", "date", "task_name"}
SUCCESS_LOG = "success.log"
ERROR_LOG = "error.log"

# === HEADERS FOR AUTH ===
headers = {
    "Authorization": f"Bearer {BEARER_TOKEN}",
    "Content-Type": "application/json",
    "User-Agent": "Glenn's People Import Integration (glenn@example.com)"
}

# === Clear previous logs ===
for log_file in [SUCCESS_LOG, ERROR_LOG]:
    if os.path.exists(log_file):
        os.remove(log_file)

# === DIAGNOSTIC: Column count per line ===
print("🔍 Inspecting column count per line in the CSV...\n")
with open(CSV_FILE) as f:
    for i, line in enumerate(f, 1):
        print(f"Line {i}: {len(line.strip().split(','))} columns")

# === READ CSV ===
try:
    df = pd.read_csv(CSV_FILE)
except Exception as e:
    print(f"❌ Error reading CSV: {e}")
    exit(1)

# === VALIDATE HEADER COLUMNS ===
missing_cols = REQUIRED_COLUMNS - set(df.columns)
if missing_cols:
    print(f"❌ Missing required columns: {', '.join(missing_cols)}")
    exit(1)

# === SEND DATA ===
for index, row in df.iterrows():
    row_num = index + 1

    # Skip if any required field is missing
    if pd.isnull(row["people_id"]) or pd.isnull(row["project_id"]) or pd.isnull(row["hours"]) or pd.isnull(row["date"]):
        with open(ERROR_LOG, "a") as elog:
            elog.write(f"[Row {row_num}] Skipped (missing required data): {row.to_dict()}\n")
        print(f"⚠️ Row {row_num}: Skipped (missing required data)")
        continue

    # Ensure task_name is a string, even if missing
    task_name = "" if pd.isnull(row.get("task_name")) else str(row["task_name"])

    # Prepare payload
    payload = {
        "people_id": int(row["people_id"]),
        "project_id": int(row["project_id"]),
        "hours": float(row["hours"]),
        "date": str(row["date"]),
        "task_name": task_name
    }

    if DRY_RUN:
        print(f"[Dry-run] Row {row_num}: {payload}")
    else:
        try:
            response = requests.post(FLOAT_API_URL, json=payload, headers=headers)
            error_text = response.text.strip()

            if response.status_code in [200, 201]:
                with open(SUCCESS_LOG, "a") as slog:
                    slog.write(f"[Row {row_num}] Success: {payload}\n")
                print(f"✅ Row {row_num}: Success")
            else:
                with open(ERROR_LOG, "a") as elog:
                    elog.write(f"[Row {row_num}] Error {response.status_code}: {error_text} | Payload: {payload}\n")
                print(f"❌ Row {row_num}: API Error {response.status_code} – {error_text}")
        except Exception as e:
            with open(ERROR_LOG, "a") as elog:
                elog.write(f"[Row {row_num}] Exception: {e} | Payload: {payload}\n")
            print(f"❌ Row {row_num}: Exception occurred – {e}")

    time.sleep(0.75)

Replace:

your_float_api_key_here with your real token.
your@email.com with your email
Your Name's Float Import Tool with your name

Save the file as import_to_float.py

Step 6: Run the script

In the terminal:

Navigate to the folder containing your files:
```
cd path/to/your/files
```
Run the script in dry-run mode first:
```
python3 import_to_float.py
```
You will see what would be sent without actually sending anything.
When ready, open the file and change:
```
DRY_RUN = False
```
Save and re-run:
```
python3 import_to_float.py
```
📝Note: Make sure to save import_to_float.py in the same directory as your import files or use the full path here

Step 7: Review the logs

success.log: shows entries that were successfully sent.
error.log: shows any failed entries and why they failed.

Example:

Need more help?

If you have questions or need help formatting your CSV, contact our support team. We are happy to assist!

Import logged time data from a CSV file via API