新建
twitter_data_initial_exploration.ipynb
xuxh@dp.tech
推荐镜像 :Basic Image:bohrium-notebook:2023-04-07
推荐机型 :c2_m4_cpu
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目录
Data Exploration
©️ Copyright 2023 @ Authors
作者：AlexZhangji
共享协议：本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。
快速开始：点击上方的 开始连接 按钮，选择 bohrium-notebook:2023-04-07镜像及任意CPU节点配置，稍等片刻即可运行。
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import pandas as pd
df = pd.read_json('data/sample_output_json.json ', lines=True)
# Convert 'date' column to datetime with the correct format
df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')
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df.date.min(), df.date.max()
(Timestamp('2021-06-04 00:00:00'), Timestamp('2024-03-01 00:00:00'))代码
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df.head(2).to_dict()
{'text': {0: 'Towards Long Context RAG \n\nGemini 1.5 Pro is impressive. Naturally this begs this question of what RAG will look like in a long-context LLM future - which techniques will disappear and which will remain?\n\nWe did a deep dive into Gemini, and consolidated our thinking about…',
, 1: 'LayerDiffusion 这个可以直接生成透明背景图片的项目已经可以在forge的扩展上使用了，试了一下确实非常牛批。\n\n不仅支持直接生成透明的图片元素，还支持在已有图片上生成跟环境融合的透明图片，这个就可以做很多事情了。\n\n不得不怀疑老哥只支持forge是为了推广他的项目，哈哈…'},
, 'author_name': {0: 'LlamaIndex', 1: '歸藏'},
, 'author_handle': {0: '@llama_index', 1: '@op7418'},
, 'date': {0: Timestamp('2024-03-01 00:00:00'),
, 1: Timestamp('2024-03-01 00:00:00')},
, 'lang': {0: 'en', 1: 'zh'},
, 'url': {0: 'https://twitter.com/llama_index/status/1763620476847632744',
, 1: 'https://twitter.com/op7418/status/1763563619193856252'},
, 'mentioned_urls': {0: [], 1: []},
, 'is_retweet': {0: False, 1: False},
, 'media_type': {0: 'Image', 1: 'Video'},
, 'images_urls': {0: ['https://pbs.twimg.com/media/GHmjUsMaEAA5hRQ?format=png&name=900x900'],
, 1: None},
, 'num_reply': {0: 3, 1: 7},
, 'num_retweet': {0: 69, 1: 71},
, 'num_like': {0: 298, 1: 267},
, 'num_view': {0: 0, 1: 0},
, 'WeekdayName': {0: 'Friday', 1: 'Friday'},
, 'WeekdayNumber': {0: 4, 1: 4}}代码
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df.sample(7)
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| text | author_name | author_handle | date | lang | url | mentioned_urls | is_retweet | media_type | images_urls | num_reply | num_retweet | num_like | num_view | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 660 | BEIR is a *zero-shot test* set. It’s not meant... | Omar Khattab | @lateinteraction | 2023-07-16 | en | https://twitter.com/lateinteraction/status/168... | [] | False | No media | None | 2 | 1 | 33 | 0 |
| 484 | Out Of Context Football | @nocontextfooty | 2023-09-25 | https://twitter.com/nocontextfooty/status/1706... | [] | False | Video | None | 367 | 6482 | 85899 | 0 | ||
| 379 | Here's my conversation with \n@elonmusk\n, his... | Lex Fridman | @lexfridman | 2023-11-09 | en | https://twitter.com/lexfridman/status/17226860... | [] | False | Video | None | 3153 | 6709 | 34571 | 0 |
| 395 | gaut | @0xgaut | 2023-11-03 | https://twitter.com/0xgaut/status/172053180438... | [] | False | Image | [https://pbs.twimg.com/media/F-COjmwXAAEHzyB?f... | 311 | 1450 | 11053 | 0 | ||
| 138 | Azure 的 zh-CN-XiaoxiaoDialectsNeural 这个语音是同一个人... | LE | @lewangx | 2024-01-16 | zh | https://twitter.com/lewangx/status/17471196773... | [] | False | Video | None | 9 | 62 | 193 | 0 |
| 563 | みなとみらいでポケモンドローンのタイムラプス | k_kinukawa | @k_kinukawa | 2023-08-11 | ja | https://twitter.com/k_kinukawa/status/16899922... | [] | False | Video | None | 42 | 19295 | 88132 | 0 |
| 739 | Every science and tech person who is currently... | Yishan | @yishan | 2023-06-19 | en | https://twitter.com/yishan/status/167061296506... | [] | False | No media | None | 607 | 224 | 1420 | 0 |
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df.author_name.value_counts().head(5)
author_name ,Jim Fan 23 ,Jerry Liu 20 ,Yann LeCun 19 ,Science girl 18 ,Massimo 17 ,Name: count, dtype: int64
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Plots
Few basic plots.
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import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
def plot_likes_with_cumulative(df, start_date, end_date):
df['date'] = pd.to_datetime(df['date'])
df = df[(df['date'] >= start_date) & (df['date'] <= end_date)]
df['media_type'] = df['media_type'].replace({'No media': 'Text'})
df['year_month'] = df['date'].dt.strftime('%Y-%m')
# Prepare the monthly count DataFrame
monthly_likes_df = df.groupby(['year_month', 'media_type']).size().reset_index(name='counts')
# Prepare the cumulative DataFrame
cumulative_likes_df = monthly_likes_df.copy()
cumulative_likes_df['cumulative_counts'] = cumulative_likes_df.groupby('media_type')['counts'].cumsum()
# Select a color palette
color_palette = px.colors.qualitative.Pastel
# Match colors to media type
color_map = {media_type: color_palette[i] for i, media_type in enumerate([ 'Video', 'Image', 'Text'])}
# Creating subplots
fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
subplot_titles=("Monthly Likes by Media Type", "Cumulative Likes by Media Type"),
vertical_spacing=0.15)
# Adding the monthly bar chart
for media_type in monthly_likes_df['media_type'].unique():
filtered_df = monthly_likes_df[monthly_likes_df['media_type'] == media_type]
fig.add_trace(go.Bar(x=filtered_df['year_month'], y=filtered_df['counts'], opacity=0.8,
name=media_type, marker_color=color_map[media_type]),
row=1, col=1)
# Adding the cumulative area chart
for media_type in cumulative_likes_df['media_type'].unique():
filtered_cumulative_df = cumulative_likes_df[cumulative_likes_df['media_type'] == media_type]
fig.add_trace(go.Scatter(x=filtered_cumulative_df['year_month'], y=filtered_cumulative_df['cumulative_counts'],
mode='lines', name=media_type, fill='tonexty',
line=dict(color=color_map[media_type])),
row=2, col=1)
# Update layout for clarity and visual appeal
fig.update_layout(height=600, title_text="Likes Analysis by Media Type")
fig.update_xaxes(title_text="Month-Year", row=2, col=1)
fig.update_yaxes(title_text="Monthly Counts", row=1, col=1)
fig.update_yaxes(title_text="Cumulative Counts", row=2, col=1)
fig.show()
# Call the function with your DataFrame and a date range
plot_likes_with_cumulative(df, '2023-01-01', '2023-12-31')
C:\Users\zjxen\AppData\Local\Temp\ipykernel_46600\3397730805.py:9: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['media_type'] = df['media_type'].replace({'No media': 'Text'}) C:\Users\zjxen\AppData\Local\Temp\ipykernel_46600\3397730805.py:10: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['year_month'] = df['date'].dt.strftime('%Y-%m')
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def plot_combined_donut_chart_ordered_correctly(data_df, date_col = 'StrTime'):
# Convert 'StrTime' to datetime and extract the weekday name and number
data_df['WeekdayName'] = data_df[date_col].dt.day_name()
# Map weekday names to numbers for sorting (Monday=0, ..., Sunday=6)
weekdays_map = {'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3, 'Friday': 4, 'Saturday': 5, 'Sunday': 6}
data_df['WeekdayNumber'] = data_df['WeekdayName'].map(weekdays_map)
# Prepare the data: count messages by WeekdayName, ignoring sender
weekday_distribution = data_df.groupby(['WeekdayName', 'WeekdayNumber']).size().reset_index(name='Count')
# Sort by WeekdayNumber to ensure correct order
weekday_distribution.sort_values('WeekdayNumber', inplace=True)
# Now that sorting is done, WeekdayNumber is no longer needed
weekday_distribution = weekday_distribution.drop(columns=['WeekdayNumber'])
# Create Donut chart with a different color scheme
fig = px.pie(weekday_distribution, names='WeekdayName', values='Count',
title="Tweets Liked by Weekday",
color_discrete_sequence=px.colors.qualitative.Pastel,
hole=0.4)
# Add slight margin between each day for better visualization
fig.update_traces(textinfo='percent+label', pull=0.02)
fig.update_traces(
sort=False,
direction='clockwise'
)
fig.show()
# Ensure data_df['StrTime'] is a datetime column before calling the function
plot_combined_donut_chart_ordered_correctly(df, date_col='date')
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from plotly_calplot import calplot
def convert_to_day_df(df):
# Convert the 'date' column to datetime if it's not already
df['date'] = pd.to_datetime(df['date'])
day_df = df.groupby(df['date'].dt.date).size().reset_index(name='num_tweets')
return day_df
day_df = convert_to_day_df(df)
day_df.head(2)
fig = calplot(day_df[(day_df['date'].astype(str)>'2023-01-01') & (day_df['date'].astype(str) < '2024-01-01')], x="date", y="num_tweets",
start_month = 1,
end_month = 12,
)
# set height of the figure
fig.update_layout(height=250)
# add title
fig.update_layout(title='Number of Liked Tweets per Day 2023')
fig.show()
c:\Users\zjxen\anaconda3\envs\openai\lib\site-packages\plotly_calplot\calplot.py:159: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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Image captions
Proof of concept: get high level summary, detailed description and tags for first image from liked tweets.
With GPT-4-V.
Gemini 1.0 also works pretty great for this usecase.
Mainly, convert image to structured json data.
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from openai import OpenAI
from config import OPENAI_API_KEY
import os
import json
from IPython.display import Image, display
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
client = OpenAI()
def get_valid_json_with_gpt35(input):
response = client.chat.completions.create(
model="gpt-3.5-turbo-0125", # latest version of GPT-3.5, better JSON mode support.
messages=[
{
"role": "system",
"content": "You will be given a JSON string (that may not be completely valid), your task is to create a valid JSON results based on the input. Try keep the structure of the input JSON as much as possible."
},
{
"role": "user",
"content": input
}
],
temperature=0.1,
response_format = { "type": "json_object" },
max_tokens=2000,
)
return response.choices[0].message.content
def get_tweet_image_captioning(image_url, author_name, text):
image_prompt = f"""
This is a tweet from {author_name},
with the following text:
{text}.
Below is an image from the tweet.
Your task is to caption the image and provide a description. reference the text or author if necessary.
Output rules you MUST follow:
- "Summary": A brief high level, big picture description of the image. What is this and what is this for?
- "Description": A detailed description of the image. If there are text in the image, please include all of it in the description. (in its original language)
- "Tags": a brief list of high level tags that describe the image. (e.g. "cat", "anime girl", "food")
Return results in a JSON format (with "Answer" as key, contains list of dictionary with exactly these key names):
"""
vision_response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": image_prompt},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
}
],
max_tokens=1000,
)
# Post process the response with GPT 3.5 json mode
json_captions = get_valid_json_with_gpt35(vision_response.choices[0].message.content)
return json_captions
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sample_image_df = df[df.media_type=='Image'].head(30)
sample_image_urls = sample_image_df.images_urls.tolist()
sample_authors = sample_image_df.author_name.tolist()
sample_texts = sample_image_df.text.tolist()
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cur_idx = 26
image_url = sample_image_urls[cur_idx][0]
author_name = sample_authors[cur_idx]
text = sample_texts[cur_idx]
display(Image(url=image_url))
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print(f"Image url: {image_url}\n"
f"Author name: {author_name}\n"
f"Text: {text}" )
result = get_tweet_image_captioning(image_url, author_name, text)
Image url: https://pbs.twimg.com/media/GGQj7ObbQAAaKgl?format=jpg&name=900x900 Author name: Kars Text: Cover for Fate/Strange Fake volume 9 which will release on March 8. https://dengekibunko.jp/product/fate/322308000304.html… #strangefake
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print(json.dumps(json.loads(result), indent=4))
{
"Answer": [
{
"Summary": "The image is an illustrated cover art for 'Fate/Strange Fake' volume 9 which is set to release on March 8 as mentioned in the tweet by Kars.",
"Description": "The image features an anime-style illustration of a red-haired female character with a determined expression. She is wearing a white Greek-style toga with a purple sash, which appears to be unraveling, and black and white armored gloves. The character has a red choker and earrings, and blood is visible on her shoulder. Behind her, a chaotic burst illustrates a dramatic scene with dark red visual effects that resemble flames or shards of destruction, contrasting against the brighter colors in the foreground. There is no text within the image itself.",
"Tags": [
"anime",
"book cover",
"Fate/Strange Fake",
"illustration",
"red-haired anime character"
]
}
]
}
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Download images
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df.head(2)
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| text | author_name | author_handle | date | lang | url | mentioned_urls | is_retweet | media_type | images_urls | num_reply | num_retweet | num_like | num_view | WeekdayName | WeekdayNumber | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Towards Long Context RAG \n\nGemini 1.5 Pro is... | LlamaIndex | @llama_index | 2024-03-01 | en | https://twitter.com/llama_index/status/1763620... | [] | False | Image | [https://pbs.twimg.com/media/GHmjUsMaEAA5hRQ?f... | 3 | 69 | 298 | 0 | Friday | 4 |
| 1 | LayerDiffusion 这个可以直接生成透明背景图片的项目已经可以在forge的扩展上... | 歸藏 | @op7418 | 2024-03-01 | zh | https://twitter.com/op7418/status/176356361919... | [] | False | Video | None | 7 | 71 | 267 | 0 | Friday | 4 |
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import os
import requests
import time
from urllib.parse import urlparse
def download_images(df, output_dir='downloaded_images'):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for _, row in df.iterrows():
if row['images_urls'] is not None:
url_parts = urlparse(row['url'])
twitter_name = url_parts.path.split('/')[1]
tweet_id = url_parts.path.split('/')[-1]
for i, image_url in enumerate(row['images_urls'], start=1):
image_id = f"{twitter_name}__{tweet_id}_{i}"
image_path = os.path.join(output_dir, f"{image_id}.jpg")
try:
response = requests.get(image_url, stream=True)
response.raise_for_status()
with open(image_path, 'wb') as file:
for chunk in response.iter_content(chunk_size=8192):
file.write(chunk)
print(f"Downloaded image: {image_id}")
time.sleep(1) # Add a delay to avoid being banned by Twitter
except requests.exceptions.RequestException as e:
print(f"Error downloading image: {image_id}")
print(f"Error message: {str(e)}")
print("Image download completed.")
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df.shape
(1026, 16)
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download_images(df, output_dir='downloaded_images')
Downloaded image: llama_index__1763620476847632744_1 Downloaded image: MadSealCutter__1763107184580112694_1 Downloaded image: sophiamyang__1762125912051519947_1 Downloaded image: Sentdex__1761574584959095097_1 Downloaded image: OrwellNGoode__1760960242915930329_1 Downloaded image: LostLeanore__1760738125918425471_1 Downloaded image: FedeItaliano76__1760596748764917765_1 Downloaded image: FedeItaliano76__1760596748764917765_2 Downloaded image: FedeItaliano76__1760596748764917765_3 Downloaded image: FedeItaliano76__1760596748764917765_4 Downloaded image: chrisalbon__1760760129300070602_1 Downloaded image: DrJimFan__1760695377651781950_1 Downloaded image: DrJimFan__1760695377651781950_2 Downloaded image: DrJimFan__1760695377651781950_3 Downloaded image: DrJimFan__1760695377651781950_4 Downloaded image: elonmusk__1759295781196927438_1 Downloaded image: elonmusk__1759295781196927438_2 Downloaded image: elonmusk__1759295781196927438_3 Downloaded image: IronPineapple___1760394643265462645_1 Downloaded image: JeffDean__1760291769252762110_1 Downloaded image: ELDENRING__1760076880764449173_1 Downloaded image: karpathy__1759996549109776702_1 Downloaded image: astraorsa__1759782862931493013_1 Downloaded image: OfirPress__1759562221305090282_1 Downloaded image: EGCTV_Official__1759304647456477381_1 Downloaded image: Culture_Crit__1758876004158750857_1 Downloaded image: OpenAIDevs__1758631343595168142_1 Downloaded image: Ehco1996__1758300529237852335_1 Downloaded image: ollama__1758201836685967599_1 Downloaded image: lmsysorg__1758224805676159392_1 Downloaded image: JeffDean__1758146211029405951_1 Downloaded image: supersat__1757939524251107734_1 Downloaded image: onevisualstudio__1757653164906459361_1 Downloaded image: op7418__1757619736190607858_1 Downloaded image: op7418__1757619736190607858_2 Downloaded image: KaroshiMyriad__1757569821481525646_1 Downloaded image: AravSrinivas__1756771041790370189_1 Downloaded image: AravSrinivas__1756771041790370189_2 Downloaded image: 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