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Use pd.Index to name an index (or column) from construction. Pandas has Index (MultiIndex) objects that accepts names. Passing those as index or column on dataframe construction constructs frames with named indices/columns. data = {'Column 1': [1,2,3,4], 'Index Title': ["Apples","Oranges","Puppies","Ducks"]}
The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display. Enables automatic and explicit data alignment. Allows intuitive getting and setting of subsets of the data set.
In Pandas, an index refers to the labeled array that identifies rows or columns in a DataFrame or a Series. For example, Name Age City 0 John 25 New York 1 Alice 28 London 2 Bob 32 Paris. In the above DataFrame, the numbers 0, 1, and 2 represent the index, providing unique labels to each row.
The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display. Enables automatic and explicit data alignment. Allows intuitive getting and setting of subsets of the data set.
Index labels (the labels assigned to rows of the data) are always present in both Series and DataFrames and can be accessed through the .index attribute (e.g., my_dataframe.index), and column labels are always present in DataFrames and can be accessed through the .columns attribute (e.g., my_dataframe.columns).
In this example, we create a DataFrame with 3 rows and 3 columns, including Name, Age, and Location information. We set the index labels to be the integers 10, 20, and 30. We then access the index attribute of the DataFrame, which returns an Index object containing the index labels.
21 Αυγ 2019 · Use .loc[label_values] to select rows based on their labels. import pandas as pd df = pd.DataFrame({ 'name':['john','mary','peter','nancy','gary'], 'age':[22,33,27,22,31], 'state':['AK','DC','CA','CA','NY'] }) # select row whose label is 0 df.loc[[0]] # select rows whose labels are 2 and 3 df.loc[[2,3]] Source dataframe.
