[ Projecting and Filtering¶ Let us understand how to project as well as filter data in Data Frames. In [1]: %run 06_csv_to_pandas_data_frame.ipynb In [2]: orders Out[2]: order_id order_date order_customer_id order_status 0 1 2013-07-25 00:00:00.0 11599 CLOSED 1 2 2013-07-25 00:00:00.0 256 PENDING_PAYMENT 2 3 2013-07-25 00:00:00.0 12111 COMPLETE 3 4 2013-07-25 00:00:00.0 8827 CLOSED 4 5 2013-07-25 …
[ CSV to Pandas Data Frame¶ Let us see how we can create Pandas Data Frames using data from files. read_csv is the most popular API to create a Data Frame by reading data from files. Here are some of the important options. sep or delimiter header or names index_col dtype and many more We …
[ Data Frames – Basic Operations¶ Here are some of the basic operations we typically perform on top of Pandas Data Frame. Getting number of records and columns. Getting data types of the columns. Replacing NaN with some standard values. Dropping a column from the Data Frame. Getting or updating column names. Sorting by index …
[ Creating Data Frames from lists¶ Let us go through the details of creating Data Frames using collections. Pandas Data Frame is a two-dimensional labeled array capable of holding attributes of any data type. It is similar to multi column excel spreadsheet or a database table. We can create Data Frame using list of tuples …
[ Overview of Series¶ Let us quickly go through one of the Pandas Data Structure – Series. Pandas Series is a one-dimensional labeled array capable of holding any data type. It is similar to one column in an excel spreadsheet or a database table. We can create Series by using dict. In [1]: d = {“JAN”: …
[ Pandas Data Structures – Overview¶ Let us understand the details with respect to Pandas. Pandas is not a core Python module and hence we need to install using pip – pip install pandas. It has 2 types of data structures – Series and DataFrame. Series is a one dimension array while DataFrame is a …
