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This article introduces the relevant knowledge of "how to use Python to do data analysis". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Generate a data table

There are two common generation methods, the first is to import external data, and the second is to write data directly. Python supports importing from many types of data. Before starting to use Python for data import, we need to import the pandas library. For convenience, we also import the Numpy library. The code is the simplest mode, and there are many optional parameter settings, such as column name, index column, data format, and so on.

Check the data table

The shape function is used in Python to view the dimensions of the data table, that is, the number of rows and columns. You can use the info function to view the overall information of the data table, and use the dtypes function to return the data format. Isnull is a function that checks null values in Python. You can check the entire data table, or you can check a column for null values separately. The result returned is a logical value. If null values are included, True is returned. If not, False is returned. Use the unique function to see the unique values, and the Values function to view the values in the data table.

Data sheet cleaning

The method of dealing with null values in Python is more flexible. You can use the Dropna function to delete data that contains null values in the data table, or you can use the fillna function to fill the null values. Dtype in Python is the function to view the data format, corresponding to the astype function, which is used to change the data format, Rename is the function to change the column name, drop_duplicates function to delete duplicate values, and replace function to realize data replacement.

Data preprocessing

Data preprocessing is to sort out the cleaned data for later statistics and analysis, including data table merging, sorting, numerical division, data grouping and marking and so on. In Python, you can use the merge function to merge two data tables by inner, as well as left, right, and outer. Ort_values function and sort_index function are used to complete sorting, where function is used to complete data grouping, and split function is used to achieve sorting.

Data extraction

Three functions are mainly used: loc, iloc and ix, in which loc function is extracted by label value, iloc is extracted by location, and ix can be extracted by label and location at the same time. In addition to lifting the data by label and location, you can also carry out the data according to specific conditions, such as using loc and isin functions to extract the data according to the specified conditions.

Data filter summary

In Python, the loc function is used with the filter condition to complete the filter function, and the sum and count functions can also realize the functions of sumif and countif in excel. The main functions used in Python are groupby and pivot_table. Groupby is a function for classifying and summarizing, and the method is very simple. You can define the column names to be grouped, or you can make multiple column names at the same time. Groupby groups according to the order in which the column names appear.

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