https://github.com/caleb531/imessage-conversation-analyzer
Gathers metrics of your choice for the entire history of a macOS Messages conversation
https://github.com/caleb531/imessage-conversation-analyzer
conversation imessage pandas phone-number python texting
Last synced: 6 months ago
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Gathers metrics of your choice for the entire history of a macOS Messages conversation
- Host: GitHub
- URL: https://github.com/caleb531/imessage-conversation-analyzer
- Owner: caleb531
- License: mit
- Created: 2020-09-13T02:11:50.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-03T00:44:20.000Z (over 1 year ago)
- Last Synced: 2024-04-16T18:46:16.039Z (over 1 year ago)
- Topics: conversation, imessage, pandas, phone-number, python, texting
- Language: Python
- Homepage: https://pypi.org/project/imessage-conversation-analyzer/
- Size: 343 KB
- Stars: 21
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# iMessage Conversation Analyzer
*Copyright 2020-2025 Caleb Evans*
*Released under the MIT license*
[](https://github.com/caleb531/imessage-conversation-analyzer/actions/workflows/tests.yml)
[](https://coveralls.io/r/caleb531/imessage-conversation-analyzer?branch=main)
iMessage Conversation Analyzer (ICA) is a fully-typed Python library (and CLI
utility) that will read the contents of an iMessage conversation via the
Messages app's database on macOS. You can then gather various metrics of
interest on the messages.
Much of this program was inspired by and built using findings from [this blog post by Yorgos Askalidis][blog-post].
[blog-post]: https://medium.com/@yaskalidis/heres-how-you-can-access-your-entire-imessage-history-on-your-mac-f8878276c6e9
### Caveats
- Group chats (three or more people) are not supported at this time
- This program only runs on macOS
## Installation
Open a Terminal and run the following:
```sh
pip3 install imessage-conversation-analyzer
```
## Usage
The package includes both a Command Line API for simplicity/convenience, as well
as a Python API for developers who want maximum flexibility.
### Command Line API
To use ICA from the command line, run the `ica` command from the Terminal. The
minimum required arguments are:
1. A path to an analyzer file to run, or the name of a built-in analyzer
2. The first and last name of the contact, via the `-c`/`--contact` flag
1. If the contact has no last name on record, you can just pass the first
name
#### Example
```sh
ica message_totals -c 'Jane Fernbrook'
```
The following outputs a table like:
```
Metric Total
Messages 14535
Messages From Me 7289
Messages From Them 7246
Reactions 5050
Reactions From Me 3901
Reactions From Them 1149
Days Messaged 115
Days Missed 0
Days With No Reply 0
```
#### Built-in analyzers
ICA includes several built-in analyzers out of the box:
1. `message_totals`: a summary of message and reaction counts, by person and in
total, as well as other insightful metrics
2. `attachment_totals`: lists count data by attachment type, including
number of Spotify links shared, YouTube videos, Apple Music, etc.
3. `most_frequent_emojis`: count data for the top 10 most frequently used emojis
across the entire conversation
4. `totals_by_day`: a comprehensive breakdown of message totals for every day
you and the other person have been messaging in the conversation
5. `transcript`: a full, unedited transcript of every message, including
reactions, between you and the other person (attachment files not included)
6. `count_phrases`: count the number of case-insensitive occurrences of any
arbitrary strings across all messages in a conversation (excluding reactions)
#### Filtering
There are several built-in flags you can use to filter messages and attachments.
- `--from-date`: A start date to filter messages by (inclusive); the format must
be ISO 8601-compliant, e.g. YYYY-MM-DD or YYYY-MM-DDTHH:MM:SS
- `--to-date`: An end date to filter messages by (exclusive); the format must be
ISO 8601-compliant, e.g. YYYY-MM-DD or YYYY-MM-DDTHH:MM:SS
- `--from-person`: A reference to the person by whom to filter messages;
accepted values can be `me`, `them`, or `both` (the default)
```sh
ica message_totals -c 'Jane Fernbrook' --from-date 2024-12-01 --to-date 2025-01-01 --from-person them
```
#### Other formats
You can optionally pass the `-f`/`--format` flag to output to a specific format
like CSV (supported formats include `csv`, `excel`/`xlsx`, and `markdown`/`md`).
```sh
ica message_totals -c 'Jane Fernbrook' -f csv
```
```sh
ica ./my_custom_analyzer.py -c 'Jane Fernbrook' -f csv
```
#### Writing to a file
Finally, there is an optional `-o`/`--output` flag if you want to output to a
specified file. ICA will do its best to infer the format from the file
extension, although you could also pass `--format` if you have special filename
requirements.
```sh
ica transcript -c 'Thomas Riverstone' -o ./my_transcript.xlsx
```
### Python API
The Python API is much more powerful, allowing you to integrate ICA into any
type of Python project that can run on macOS. All of the built-in analyzers
(under the `ica/analyzers` directory) actually use this API.
Here's a complete example that shows how to retrieve the transcript of an entire
iMessage conversation with one other person.
```python
# get_my_transcript.py
import pandas as pd
import ica
# Export a transcript of the entire conversation
def main() -> None:
# Allow your program to accept all the same CLI arguments as the `ica`
# command; you can skip calling this if have other means of specifying the
# contact name and output format; you can also add your own arguments this
# way (see the count_phrases analyzer for an example of this)
cli_args = ica.get_cli_parser().parse_args()
# Retrieve the dataframes corresponding to the processed contents of the
# database; dataframes include `messages` and `attachments`
dfs = ica.get_dataframes(
contact_name=cli_args.contact_name,
timezone=cli_args.timezone,
from_date=cli_args.from_date,
to_date=cli_args.to_date,
from_person=cli_args.from_person,
)
# Send the results to stdout (or to file) in the given format
ica.output_results(
pd.DataFrame(
{
"timestamp": dfs.messages["datetime"],
"is_from_me": dfs.messages["is_from_me"],
"is_reaction": dfs.messages["is_reaction"],
# U+FFFC is the object replacement character, which appears as
# the textual message for every attachment
"message": dfs.messages["text"].replace(
r"\ufffc", "(attachment)", regex=True
),
}
),
# The default format (None) corresponds to the pandas default dataframe
# table format
format=cli_args.format,
# When output is None (the default), ICA will print to stdout
output=cli_args.output,
)
if __name__ == "__main__":
main()
```
You can run the above program using the `ica` command, or execute it directly
like any other Python program.
```sh
ica ./get_my_transcript.py -c 'Thomas Riverstone'
```
```sh
python ./get_my_transcript.py -c 'Thomas Riverstone'
```
```sh
python -m get_my_transcript -c 'Thomas Riverstone'
```
You're not limited to writing a command line program, though! The
`ica.get_dataframes()` function is the only function you will need in any
analyzer program. But beyond that, feel free to import other modules, send your
results to other processes, or whatever you need to do!
### Errors and exceptions
- `BaseAnalyzerException`: the base exception class for all library-related
errors and exceptions
- `ContactNotFoundError`: raised if the specified contact was not found
- `ConversationNotFoundError`: raised if the specified conversation was not
found
- `FormatNotSupportedError`: raised if the specified format is not supported by
the library
#### Using a specific timezone
By default, all dates and times are in the local timezone of the system on which
ICA is run. If you'd like to change this, you can pass the `--timezone` / `-t`
option to the CLI with an IANA timezone name.
```sh
ica totals_by_day -c 'Daniel Brightingale' -t UTC
```
```sh
ica totals_by_day -c 'Daniel Brightingale' -t America/New_York
```
The equivalent option for the Python API is the `timezone` parameter to
`ica.get_dataframes`:
```python
dfs = ica.get_dataframes(contact_name=my_contact_name, timezone='UTC')
```
## Developer Setup
The following instructions are written for developers who want to run the
package locally, or write their own analyzers.
### 1. Set up virtualenv
```sh
pip3 install virtualenv
```
```sh
virtualenv --python=python3 .virtualenv
source .virtualenv/bin/activate
```
### 2. Install project depdendencies
```sh
pip install -r requirements.txt
```