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This article mainly explains "how to set up Pandas to do data analysis", the content of the article is simple and clear, easy to learn and understand, now please follow the editor's ideas slowly in depth, together to study and learn "how to set Pandas to do data analysis" bar!

1. Set the maximum number of rows displayed in DataFrame

The display.max_rows in the pandas setting parameter is used to control the maximum number of rows displayed in the printed data box. We use pd.set_option () to set the parameters for this, as in the following example:

Figure 2

After changing the parameter value of display.max_rows, our data box will only display the specified number of rows of data, the middle part will be shown in the form of ellipsis, when our data box has more rows, we can increase this parameter to display more rows of data.

two。 Set the maximum number of display columns for DataFrame

Similar to display.max_rows, we can adjust the maximum number of data box columns displayed (default is 20 columns) by modifying display.max_columns, which is useful when we have a large number of data box fields and want to view all of them:

Figure 3

3. Set the maximum display width for each column

For some data with long cell content, such as long text, the long part of the data box will be simplified to an ellipsis when viewing the data box, and by modifying the display.max_colwidth parameter, we can make the extra long part also appear if necessary:

Figure 4

4. Specifies that elements less than a certain number are displayed as 0

Through the display.chop_threshold parameter, without modifying the original data, the number of absolute values in the specified data box that are less than the threshold is displayed as 0:

Figure 5

5. Formatted floating point number

We can set the display format of floating-point numbers through the display.float_format parameter. For example, here we prefix floating-point numbers with ¥and set to retain two decimal places:

Figure 6

6. Sets the upper limit of the number of rows for non-missing value checks in the info () method

The info () method for the data box can help us see some overview information about the data box, such as the number of non-missing values corresponding to each column.

However, by default, when the number of rows in the data box is greater than 1690784, looking at the info () information will omit the missing value check information for the sake of computational efficiency.

At this point, we can increase this upper limit by setting the display.max_info_rows parameter:

Figure 7

7. Control the accuracy of decimal printing

In addition to the methods mentioned above, the display accuracy of decimal places in the control data box can also be controlled by modifying the display.precision parameter. The default is 6 decimal places:

Figure 8

8. Temporarily modify parameters

Sometimes we only want to change the setting parameters on a table and do not want to affect the display of other tables later.

In addition to restoring the specified parameter with pd.reset_option (), we can also use the with keyword with pd.option_context to temporarily apply the specified parameter to the local range:

Thank you for your reading, the above is the content of "how to set up Pandas for data analysis". After the study of this article, I believe you have a deeper understanding of how to set up Pandas for data analysis, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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