当前位置：网站首页>[original dry goods] found a useful tool for data analysis

[original dry goods] found a useful tool for data analysis

2022-07-23 07:54:00 【Xinyi 2002】

Xiaobian recently encountered a sharp tool for data analysis , We can display the data we need on the web , And carry out relatively deep data analysis and mining , So I plan to take this opportunity to share with you .

About streamlit-aggrid

We know how to use Streamlit Module to do web Application development is really very convenient , However, it is very simple in displaying tables , There are only two simple interface functions , Namely st.table(df) and st.dataframe(df), The display of table data with many fields is very unfriendly , Today, Xiaobian will introduce one Streamlit Plug in for ,streamlit-aggrid, Its basic functions include

Data sorting
Adjustment of table style
Data filtering
Page turning
wait

First we go through pip Command to download the module

pip install streamlit-aggrid

Let's write a simple demo, Let's see what functions this module can achieve , The code is as follows

import pandas as pd 
import streamlit as st 
from st_aggrid import AgGrid

st.set_page_config(page_title=" Net flight (Netflix) Film data analysis ", layout="wide") 
st.title(" Net flight (Netflix) Film data analysis ")
shows = pd.read_csv("netflix_titles.csv")  
AgGrid(shows)

output

We and st.dataframe(shows) Compared with the results , Discovery call streamlit-aggrid The table displayed by the module is more beautiful , As shown in the figure below

Comparison of results of different methods

Of course, we can also sort the data , As shown in the figure below

And you can also filter the data according to the specified conditions , As shown in the figure below

We can also drag and drop the data of each column in the table according to our preferences , Adjust the order of the tables

More operations

Page turning

In addition to the above basic operations ,streamlit-aggrid The table data displayed by the module also supports page turning , The code is as follows

import pandas as pd
import streamlit as st
from st_aggrid import AgGrid
from st_aggrid.grid_options_builder import GridOptionsBuilder

st.set_page_config(page_title=" Net flight (Netflix) Film data analysis ", layout="wide")
st.title(" Net flight (Netflix) Film data analysis ")

shows = pd.read_csv("netflix_titles.csv")

gb = GridOptionsBuilder.from_dataframe(shows)
gb.configure_pagination()
gridOptions = gb.build()

AgGrid(shows, gridOptions=gridOptions)

output

Grouping statistics

We are usually in Pandas Used in the module groupby Group statistics streamlit-aggrid Modules can also be easily implemented , The code is as follows

import pandas as pd
import streamlit as st
from st_aggrid import AgGrid
from st_aggrid.grid_options_builder import GridOptionsBuilder

st.set_page_config(page_title=" Net flight (Netflix) Film data analysis ", layout="wide")
st.title(" Net flight (Netflix) Film data analysis ")

shows = pd.read_csv("netflix_titles.csv")
gb = GridOptionsBuilder.from_dataframe(shows)

gb.configure_pagination()
gb.configure_side_bar()
gb.configure_default_column(groupable=True, value=True, enableRowGroup=True, aggFunc="sum", editable=True)
gridOptions = gb.build()

AgGrid(shows, gridOptions=gridOptions, enable_enterprise_modules=True)

such , The toolbar will appear on the far left of the table , We can further operate in it , As shown in the figure below

Highlight table data

stay Pandas In the module, we can highlight the specified data , So in the same way streamlit-aggrid Modules can also be implemented , The code is as follows

shows = pd.read_csv("netflix_titles.csv")
gb = GridOptionsBuilder.from_dataframe(shows)

cellsytle_jscode = JsCode(
    """
function(params) {
    if (params.value.includes('United States')) {
        return {
            'color': 'white',
            'backgroundColor': 'red'
        }
    } else {
        return {
            'color': 'black',
            'backgroundColor': 'white'
        }
    }
};
"""
)

gb.configure_column("country", cellStyle=cellsytle_jscode)
gridOptions = gb.build()
data = AgGrid(
    shows,
    gridOptions=gridOptions,
    enable_enterprise_modules=True,
    allow_unsafe_jscode=True
)

We call the country “ The United States ” The movie data of is highlighted in red , As shown in the figure below