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This article mainly introduces the use of DataFrame in Pandas, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Getting started exampl

Code block:

# basic operation of Pandas DataFrame import pandas as pdimport numpy as np# In [45]: data = {'Day': [1 Day': 2, 3, 4, 5, 5, 6, 7],' Visits': [23, 45, 12, 46, 88, 45, 98] 'Rates': [1.0In 2.1meme 2.5meme 2.2meme 4.3meme 4.5Magne5.0]} # use DataFrame to load data # In [46]: df = pd.DataFrame (data) # In [47]: df# View the first five pieces of data # In [48]: df.head () # View the last five pieces of data # In [49]: df.tail () # View the last two pieces of data # In [ 50]: df.tail (2) # use set_index () to set the index column of dataframe # In [51]: df.set_index ('Day') # # We continue to print the first five pieces of data # found that the index has not been changed to the Day# set above because using df.set_index (' Day') creates a new object # In [52]: df.head by default () # modify the code as above to make the index take effect # In [53]: df2 = df.set_index ('Day') df2.head () # We use the parameter inplace=True to do the same thing # means to modify the DataFrame without creating a new object # In [54]: df.set_index (' Day') Inplace=True) df.head () # print Visits column values # In [55]: df = pd.DataFrame (data) df ['Visits'] # In [56]: df.Visits# print both Visits and Rates values # In [57]: df [[' Visits'] 'Rates']] # convert the value of the Visits column to list# In [58]: df.Visits.tolist () # convert the Visits and Rates columns into the numpy array # In [59]: np.array (df [[' Visits','Rates']]) # load the numpy array into DataFrame# In [60]: df_new = pd.DataFrame (df [['Visits']) 'Rates']]) df_new thanks you for reading this article carefully I hope the article "what is the use of DataFrame in Pandas" shared by the editor will be helpful to you. At the same time, I also hope that you will support us and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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