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This article mainly introduces "what is the visual operation method of python dataframe". In the daily operation, I believe that many people have doubts about what the visual operation method of python dataframe is. The editor consulted all kinds of data and sorted out a simple and easy-to-use operation method. I hope it will be helpful for you to answer the doubt of "what is the visual operation method of python dataframe?" Next, please follow the editor to study!

Dataframe Visualization Operation pyplot express example

# # using pyplot express

Import plotly_express as px

Fig = px.scatter (df_v1, x = "ds", y = "order groups")

Fig.update_yaxes (rangemode= "tozero", tickformat='.')

Fig.update_xaxes (tickangle=45, tickformat='%Y-%m-%d')

Fig.show () uses pyplot express documents

Step1: you can directly load the dataframe data box, the corresponding fields of x-axis and y-axis.

Step2: you can use fig.update_yaxes,fig.update_xaxes to update the layout, that is, to update the layout of ployly

Key note: fig.update_yaxes,fig.update_xaxes setting is equivalent to plotly layout setting, setting X axis and y axis respectively

For detailed layout layout settings, please consult the reference documentation.

Mark:

Y-axis disable scientific counting: the main setting fields are: tickformat

The X-axis date is displayed normally: the main setting field is also: tickformat (string)

The Y axis is displayed from the zero scale or freely

Import plotly.express as px

Iris = px.data.iris ()

Fig = px.scatter (iris, x = "sepal_width", y = "sepal_length", facet_col= "species")

Fig.update_xaxes (rangemode= "tozero") # display from 0

Fig.update_yaxes (rangemode= "tozero") # display from 0

Fig.show ()

The X axis sets the tilt angle

Import plotly.express as px

Tips = px.data.tips ()

Fig = px.histogram (tips, x = "sex", y = "tip", histfunc='sum', facet_col='smoker')

Fig.update_xaxes (tickangle=45, tickfont=dict (family='Rockwell', color='crimson', size=14))

Fig.show () at this point, the study of "what is the visual operation method of python dataframe" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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