Thepandas module lets you parse data. For instance, you can have excel data that you want to read.
You can load an excel file with the method read_excel(filename), where filename may include a path. It can read both xls and xlsx.
That data is stored in a data frame. The data frame is a data structure in pandas, which you can edit orplot.
#!/usr/bin/python3# coding: utf-8import pandas as pddf = pd.read_excel('example.xls')data1 = df.head(7)data2 = df.valuesprint("A \n{0}".format(data1))print("B \n{0}".format(data2))What we are doing here is very simple. I'll describe the steps.
Load the excel data. This file has to be in the same directory, else a path must be specified.
df = pd.read_excel('example.xls')Store data from the data frame into variables
data1 = df.head(7)data2 = df.valuesOutput those variables. Because it's not a single value we format it.
print("A \n{0}".format(data1))print("B \n{0}".format(data2))Run it in a terminal (or IDE if you prefer)
python3 example.pyOutputs the data from the excel:
A id name class date stature0 201901 Aaron 1 2019-01-01 11 201902 Arthur 1 2019-01-02 12 201903 Angus 1 2019-01-03 13 201904 Albert 2 2019-01-04 24 201905 Adrian 2 2019-01-05 25 201906 Adam 3 2019-01-06 16 201907 Andres 3 2019-01-07 1B [[201901 'Aaron' 1 Timestamp('2019-01-01 00:00:00') 1] [201902 'Arthur' 1 Timestamp('2019-01-02 00:00:00') 1] [201903 'Angus' 1 Timestamp('2019-01-03 00:00:00') 1] [201904 'Albert' 2 Timestamp('2019-01-04 00:00:00') 2] [201905 'Adrian' 2 Timestamp('2019-01-05 00:00:00') 2] [201906 'Adam' 3 Timestamp('2019-01-06 00:00:00') 1] [201907 'Andres' 3 Timestamp('2019-01-07 00:00:00') 1] [201908 'Alex' 3 Timestamp('2019-01-08 00:00:00') 1]]Related links:
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