Skip to main content

How to plot Pandas Dataframe as a Table in Python using Plotly?

Sometime its necessary to plot the pandas Dataframe as a table in python/Jupyter notebook, for instance for an easy reference to the Data dictionary. This is made easy by using plotly.

Step 1: Install Plolty 

pip install plotly

Step 2: Import Plotly

import plotly.graph_objects as go

Step 3:  Plot the table 

# read the data dictonary
data_dict = pd.read_csv("data_dictionary.csv")
fig = go.Figure(data=[go.Table(
    header=dict(values=list(data_dict.columns),
                fill_color='paleturquoise',
                align='left'),
    cells=dict(values=data_dict.transpose().values.tolist(),
               fill_color='lavender',
               align='left'))
])

fig.show()



Comments

Popular posts from this blog

FTP C# Error : “The remote server returned an error: (530) Not logged in ."

  Recently working on a FTP solution using C# , i encountered an error  “ The  remote server returned an error: (530) Not logged in.” The code i used was following: FtpWebRequest request = (FtpWebRequest)WebRequest.Create( ftp://xxxxxx/file.txt ); request.Method = WebRequestMethods.Ftp.UploadFile request.Credentials = new NetworkCredential(usernameVariable, passwordVariable); What was more bewildering was if i modified the code to following, the solution was working fine. But this for obvious reasons is not an option as the username cannot be hardcoded //works but implausible to use in realtime solutions request.Credentials = new NetworkCredential("dmn/#gsgs", password);  Some googling revealed that special charcters create issues in the NetworkCredential Object. Hence some playing around worked for me, and it works irrespective of wether i do a FTPWebRequest or WebRequest. Solution: Instantiate NetworkCredential object with three paramters (username, pass...

Llamhub SnowflakeReader: A Loader to query and chat Snowflake Data in your LLM Applications

  Snowflake Loader for LLM Recently my second contribution to Llamaindex "SnowflakeReader" was merged to Lllamahub repository. This loader connects to Snowflake (using SQLAlchemy under the hood). The user specifies a query and extracts Document objects corresponding to the results. This loader is designed to be used as a way to load data into  LlamaIndex  and/or subsequently used as a Tool in a  LangChain  Agent.  Usage Option 1: Pass your own SQLAlchemy Engine object of the database connection Here's an example usage of the SnowflakeReader. from llama_index import download_loader SnowflakeReader = download_loader ( 'SnowflakeReader' ) reader = SnowflakeReader ( engine = your_sqlalchemy_engine , ) query = "SELECT * FROM your_table" documents = reader . load_data ( query = query ) Option 2: Pass the required parameters to esstablish Snowflake connection Here's an example usage of the SnowflakeReader. from llama_index import down...

How to Integrate Anaconda Prompt and Jupyter Notebook with CMDER

I personally find CMDER a fantastic tool and do believe its the best windows console emulator out there. I prefer make use of its  capability  to run multiple commands side by side with split panes and tabs, which helps multitasking and productivity. For those of you who aren't aware,  CMDER  is a user-friendly console emulator for Windows that offers numerous features, including a sleek and intuitive GUI, making it easy for users, including beginners, to interact with the command line.   In this blog, I'll guide you through the steps to set up CMDER to work similar to Anaconda Prompt, a popular environment for Python development.  A Step-by-Step Guide to Anaconda Prompt Integration in CMDER     Step 1: Navigate to Anaconda Prompt Before we begin, ensure you have Anaconda installed on your system. Open the Anaconda Prompt to find the location of the 'conda' executable. Step 2: Locate the 'conda' Executable In the Anaconda Prompt, enter the followi...