Work report of epidemic data analysis platform [6] visual drawing

Plotly+Dash

Using the command line pip Direct installation dash

pip install dash

Installation consists of three parts :
html、dcc、dashtable、plotly.

If you want compatibility Jupiter Related to the environment , Use :

conda install -c conda-forge -c plotly jupyter-dash

Three private open source projects , To assist in :

xinetzone/dash-xinet： simplify  Dash  Use .

SanstyleLab/plotly-dastsets： Store some data sets and display pictures .

xinetzone/sanstyle： Handle some common tasks .

pip install dash-xinet sanstyle

from dash_xinet.server import create_app, run_server
from dash_xinet.utils.nav import create_nav

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = create_app(__name__, external_stylesheets=external_stylesheets)

Dash Application use and operation ：

layout = ... #  Layout 
await run_server(app, layout, port=8050) #  Start the application 

About sanstyle
You can directly batch modify the file suffix ：

rename_suffix(root, old, new)

root
The following directories need to be modified

old
Modified file suffix , Such as .txt

new
Modified file suffix , Such as .rst

establish Elements

from sanstyle.display.html import Embed

snippet_url = 'https://dash-tests.herokuapp.com'
figure_n_slider_dash = Embed(snippet_url + '/examples/figure-n-slider',
                   className='w3-pale-blue',
                   height=500)
figure_n_slider_dash

Import plotly-dastsets

import pandas as pd
from sanstyle.github.file import lfs_url

url = lfs_url('SanstyleLab/plotly-dastsets',
              'simple/usa-agricultural-exports-2011.csv')
df = pd.read_csv(url)
df.head()

Look again plotly.

Plotly Is an interactive drawing library , Provides JavaScript、Python and R Three interfaces . We use python.

Plotly There are three different kinds of Python API, You can choose to use different methods to draw ：

Object oriented API, adopt plotly.graph_objects The chart object of the module
Data driven API, By constructing something similar JSON The data structure of
Advanced drawing interface , It is the packaging of the underlying drawing method , namely plotly.express modular

object-oriented API
Use graph_objects When the module is drawing , Users need to define canvas objects go.Figure()、 Style object go.Layout()、 Trace objects such as scatter plot go.Scatter()、 Histogram go.Bar() etc. , Combine and render .

Data driven API
Plotly The visualization of is based on JSON Data structure ,trace It's a dictionary , Contains the data and colors to be drawn 、 Linear etc. drawing instructions , Used to specify how a set of data is rendered ; Organize multiple with a list trace Constitute the data, That is to say, all the things to be shown in a chart trace;layout It's also a dictionary , Used to set the layout of the chart , Including the title 、 Font and other attributes , take data and layout Together they form a chart . This method directly corresponds to Plotly Of JavaScript Implementation of the JSON API.

$ conda create -n plotly python=3.7  #  Set up the name plotly Virtual environment for 
$ conda activate plotly  #  Activate the virtual environment 

$ pip install plotly          #  Do not specify version number 
$ pip install plotly==4.14.3  #  Specified version number 

$ conda install -c plotly=4.14.3

$ conda install -c plotly plotly-orca==1.2.1 psutil requests

import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(x=[1, 2, 3], y=[2, 3, 1]))
fig.show()

Let's look at the specific drawing steps .

Import

import warnings
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go

warnings.filterwarnings('ignore')

# china_history
#    Line chart of cumulative diagnosis in China 
def china_confirm(df):
    df0 = df[['date', 'confirm']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='confirm',
        title=' Broken line chart of domestic diagnosis ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    Newly added diagnostic line chart in China 
def china_confirm_add(df):
    df0 = df[['date', 'confirm_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='confirm_add',
        title=' Newly added diagnostic line chart in China ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    Domestic cumulative cure line chart 
def china_heal(df):
    df0 = df[['date', 'heal']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='heal',
        title=' Domestic cumulative cure line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    New healing line chart in China 
def china_heal_add(df):
    df0 = df[['date', 'heal_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='heal_add',
        title=' New healing line chart in China ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    Line chart of cumulative domestic deaths 
def china_dead(df):
    df0 = df[['date', 'dead']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='dead',
        title=' Line chart of cumulative domestic deaths ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    New death line chart in China 
def china_dead_add(df):
    df0 = df[['date', 'dead_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='dead_add',
        title=' New death line chart in China ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#   Domestic cumulative suspected line chart 
def china_suspect(df):
    df0 = df[['date', 'suspect']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='suspect',
        title=' Domestic cumulative suspected line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#    Newly added suspected line chart in China 
def china_suspect_add(df):
    df0 = df[['date', 'suspect_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='suspect_add',
        title=' Newly added suspected line chart in China ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

# owid-covid-data

#  All countries   Line chart of total cases 
def country_total_cases(df, country_name):
    df0 = df[['date', 'total_cases']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_cases',
        title=country_name + ' Line chart of total cases ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      New case line chart 
def country_new_cases(df, country_name):
    df0 = df[['date', 'new_cases']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_cases',
        title=country_name + ' Line chart of new cases ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries   Line chart of total death 
def country_total_deaths(df, country_name):
    df0 = df[['date', 'total_deaths']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_deaths',
        title=country_name + ' Line chart of total death ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      New death line chart 
def country_new_deaths(df, country_name):
    df0 = df[['date', 'new_deaths']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_deaths',
        title=country_name + ' New death line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Unit case line chart 
def country_total_cases_per_million(df, country_name):
    df0 = df[['date', 'total_cases_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_cases_per_million',
        title=country_name + ' Unit case line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line chart of newly added cases in the unit 
def country_new_cases_per_million(df, country_name):
    df0 = df[['date', 'new_cases_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_cases_per_million',
        title=country_name + ' Line chart of newly added cases in the unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Unit total death line chart 
def country_total_deaths_per_million(df, country_name):
    df0 = df[['date', 'total_deaths_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_deaths_per_million',
        title=country_name + ' Unit total death line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Unit new death line chart 
def country_new_deaths_per_million(df, country_name):
    df0 = df[['date', 'new_deaths_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_deaths_per_million',
        title=country_name + ' Unit new death line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line graph of virus spreading rate 
def country_reproduction_rate(df, country_name):
    df0 = df[['date', 'reproduction_rate']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='reproduction_rate',
        title=country_name + ' Line graph of virus spreading rate ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line chart of number of severe patients 
def country_icu_patients(df, country_name):
    df0 = df[['date', 'icu_patients']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='icu_patients',
        title=country_name + ' Line chart of number of severe patients ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Broken line chart of the number of severe patients per unit 
def country_icu_patients_per_million(df, country_name):
    df0 = df[['date', 'icu_patients_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='icu_patients_per_million',
        title=country_name + ' Broken line chart of the number of severe patients per unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line chart of nucleic acid detection quantity 
def country_total_tests(df, country_name):
    df0 = df[['date', 'icu_patients_per_million']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='icu_patients_per_million',
        title=country_name + ' Broken line chart of the number of severe patients per unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      New line chart of nucleic acid detection quantity 
def country_new_tests(df, country_name):
    df0 = df[['date', 'new_tests']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_tests',
        title=country_name + ' New line chart of nucleic acid detection quantity ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line chart of unit nucleic acid detection quantity  
def country_total_tests_per_thousand(df, country_name):
    df0 = df[['date', 'total_tests_per_thousand']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_tests_per_thousand',
        title=country_name + ' Line chart of unit nucleic acid detection quantity ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Line chart of new nucleic acid detection quantity per unit 
def country_new_tests_per_thousand(df, country_name):
    df0 = df[['date', 'new_tests_per_thousand']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_tests_per_thousand',
        title=country_name + ' Line chart of new nucleic acid detection quantity per unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Positive rate line chart 
def country_positive_rate(df, country_name):
    df0 = df[['date', 'positive_rate']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='positive_rate',
        title=country_name + ' Positive rate line chart ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Map of total vaccination amount 
def country_total_vaccinations(df, country_name):
    df0 = df[['date', 'total_vaccinations']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_vaccinations',
        title=country_name + ' Map of total vaccination amount ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Number of vaccinated people 
def country_people_vaccinated(df, country_name):
    df0 = df[['date', 'people_vaccinated']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='people_vaccinated',
        title=country_name + ' Number of vaccinated people ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      The number of people vaccinated in the whole process 
def country_people_fully_vaccinated(df, country_name):
    df0 = df[['date', 'people_fully_vaccinated']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='people_fully_vaccinated',
        title=country_name + ' The number of people vaccinated in the whole process ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Number of new vaccinations 
def country_new_vaccinations(df, country_name):
    df0 = df[['date', 'new_vaccinations']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='new_vaccinations',
        title=country_name + ' Number of new vaccinations ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Total vaccinations per unit 
def country_total_vaccinations_per_hundred(df, country_name):
    df0 = df[['date', 'total_vaccinations_per_hundred']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='total_vaccinations_per_hundred',
        title=country_name + ' Total vaccinations per unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      Number of vaccinations per unit 
def country_people_vaccinated_per_hundred(df, country_name):
    df0 = df[['date', 'people_vaccinated_per_hundred']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='people_vaccinated_per_hundred',
        title=country_name + ' Number of vaccinations per unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  All countries      The number of people vaccinated in the whole process of the unit 
def country_people_fully_vaccinated_per_hundred(df, country_name):
    df0 = df[['date', 'people_fully_vaccinated_per_hundred']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='people_fully_vaccinated_per_hundred',
        title=country_name + ' The number of people vaccinated in the whole process of the unit ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })

    return fig

#  Provinces in China 
#  The provinces      Number of confirmed cases 
def province_confirm(df, province_name):
    df0 = df[['date', 'confirm']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='confirm',
        title=province_name + ' Number of confirmed cases ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  The provinces      The number of deaths 
def province_dead(df, province_name):
    df0 = df[['date', 'dead']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='dead',
        title=province_name + ' The number of deaths ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  The provinces      The number of people cured 
def province_heal(df, province_name):
    df0 = df[['date', 'heal']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='heal',
        title=province_name + ' The number of people cured ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  The provinces      New number of confirmed cases 
def province_confirm_add(df, province_name):
    df0 = df[['date', 'confirm_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='confirm_add',
        title=province_name + ' New number of confirmed cases ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  The provinces      New cured people 
def province_heal_add(df, province_name):
    df0 = df[['date', 'heal_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='heal_add',
        title=province_name + ' New cured people ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

#  The provinces      New deaths 
def province_dead_add(df, province_name):
    df0 = df[['date', 'dead_add']].reset_index()
    df0['date'] = df0['date'].dt.date
    df0 = df0.sort_values('date', ascending=False)
    fig = px.line(
        data_frame=df0,
        x='date',
        y='dead_add',
        title=province_name + ' New deaths ',
        height=900
    )
    fig.update_layout({
    
        # 'xaxis_dtick': 1,
        'xaxis_title': ' date ',
        'yaxis_title': ' Number ',
    })
    return fig

A little bit of code for formatting

#  Date format in the original data file 
format1 = '%Y-%m-%d'
format2 = '%Y/%m/%d'


#  Date formatting 
def date_process_df1(df):
    df['date'] = pd.to_datetime(df['date'], format=format1)
    return df

def date_process_df2(df):
    df['date'] = pd.to_datetime(df['date'], format=format2)
    return df