{"id":57424,"date":"2019-06-01T17:10:50","date_gmt":"2019-06-01T11:40:50","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=57424"},"modified":"2019-06-03T16:01:24","modified_gmt":"2019-06-03T10:31:24","slug":"pandas-index-and-select","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pandas-index-and-select\/","title":{"rendered":"Pandas Index & Select Data – 4 Tricks to Solve Any Query"},"content":{"rendered":"

Indexing in pandas is a very crucial function. It lets us select and observe data according to our will and thus allows us to get one step closer to improve our data analysis. Without indexing and selection of data in Pandas, analyzing data would be extremely difficult. With the help of custom indices, we can access our data properly and also manage it efficiently.\u00a0Pandas Index and select help us to customize our data.<\/p>\n

\"Pandas<\/a><\/p>\n

Pandas\u00a0Index and Select the Data<\/h2>\n

Before we start the pandas index and select tutorial, let us import pandas:<\/p>\n

>>> import pandas as pd<\/pre>\n
Then, import a CSV<\/p>\n
>>> dataflair_df=pd.read_csv(\"http:\/\/theeventscalendar.com\/content\/uploads\/2014\/09\/test-data-venues11.csv\")\r\n>>> dataflair_df #prints the dataset<\/pre>\n
Output-<\/strong><\/p>\n
We will get the index of dataframes by .index() function<\/strong>. Let’s take a quick glance of\u00a0Pandas DataFrames Tutorial<\/a><\/strong><\/em><\/p>\n
>>> dataflair_df.index<\/pre>\n
Output-<\/strong><\/p>\n
RangeIndex(start=0, stop=12, step=1)<\/p>\n
\"Index-functions-in-Pandas<\/a><\/p>\n
1. Dataframe_name.[]<\/h3>\n
Where dataframe_name is any name you have selected for your dataframe<\/em>)- To select a specific column<\/strong>, we will use the dataframe_name.[] function<\/p>\n
>>> dataflair_df[\"VENUE CITY\"]<\/pre>\n