Dataframe is a 2D data structure. Dataframe is used to represent data in tabular format in rows and columns. It is like a spreadsheet or a sql table. Dataframe is a Pandas object. Show
To create a dataframe, we need to import pandas. Dataframe can be created using dataframe() function. The dataframe() takes one or two parameters. The first one is the data which is to be filled in the dataframe table. The data can be in form of list of lists or dictionary of lists. In case of list of lists data, the second parameter is the columns name. Create dataframe from dictionary of listsimport pandas as pd data={'Name':['Karan','Rohit','Sahil','Aryan'],'Age':[23,22,21,24]} df=pd.dataframe(data) df #print the dataframe The output will be a table having two columns named ‘Name’ and ‘Age’ with the provided data fed into the table. Create dataframe from list of listsimport pandas as pd data=[[‘Karan’,23],[‘Rohit’,22],[‘Sahil’,21],[‘Aryan’,24]] df=pd.dataframe(data,columns=[‘Name’,’Age’]) df This also gives the same output. The only difference is in the form in which the data is provided. Since the columns names are not specified earlier, it is needed to pass column names as arguments in the dataframe() function. Create customized index dataframeimport pandas as pd data={'Name':['Karan','Rohit','Sahil','Aryan'],'Age':[23,22,21,24]} df=pd.dataframe(data,index=[‘No.1’,’No.2’,’No.3’,’No.4’]) df This creates the same dataframe with indexes as mentioned in the index list.
Updated on 11-Jun-2021 12:49:41
January 31, 2022 When it comes to exploring data with Python, DataFrames make analyzing and manipulating data for analysis easy. This article will look at some of the ins and outs when it comes to working with DataFrames. Python is a powerful tool when it comes to working with data. Qualities like its scalability and variety of
libraries for data analysis and data science applications make it versatile. However, what’s often under-appreciated-but-highly-valuable about Python is the ease with which we can manipulate data with flexible data structures. One of these structures is a DataFrame. To start, it’s important to know that there are a variety of different structures that data can take.
For the majority of cases, most data are in tabular form (i.e., data structured into rows representing a single entry). You are likely already familiar with this if you’ve ever worked with an Excel spreadsheet or a SQL table. Aggregates of each of these rows, that represent a given data entry, and their properties are formed into a two-dimensional structure where titled columns consist of values
of the same property. These structures have several unique qualities:
Although these can have different names depending on the programming language or application tool being used, in Python, we call these structures DataFrames. The principal library used in working with these structures is Pandas. How do you make a DataFrame?When it comes to creating a DataFrame, you can either import it from an external file or create it yourself in Python. Method 1 — Import Data from a FileIn the real world, a dataset is often read into Python via an external source that curated it. We can find these datasets in multiple types of files, but we most commonly find them in the form of comma separated value files (CSVs). Fortunately, in the Pandas library, it has a function that works to convert the data in this format into a DataFrame called pandas.read_csv(). The only major argument that it requires is a pathway that outlines where the file exists. One pathway may be from the web (i.e., from an API or a GitHub repository)
Alternatively, if a file were stored on your computer in a working directory, then the path would adjust accordingly. In this process, we could use either the relative or full path to specify the pathway to retrieve a given file because the function can decipher the difference between the two without an issue.
7787 rows × 12 columns
Although CSV files are the most common, there are a number of different functions that are available in Pandas to read in files of various types into a DataFrame that operates with the same general process: METHOD 2 – Creating DataFrames YourselfWhile not the most common method of creating a DataFrame, you can certainly create a data frame yourself by inputting data. We can accomplish this with the
pandas.DataFrame() function, which takes its data input argument and converts it into a DataFrame. The
Although the same data types are used in the examples above (strings), DataFrames can consist of a variety of different data types, such as integers, floats, lists, datetimes, Booleans, list, etc.
Exploring a DataFrameSince Python is an object-oriented programming language, creating a DataFrame means creating an object of the DataFrame class. This also means that there are a number of different attributes that we can explore and methods that we can apply to the DataFrame. While we use these more often in situations when we aren’t familiar with the dataset (say from importing it from somewhere), they are nonetheless useful. Whenever a dataset is loaded into Python as a DataFrame, it’s best to look at its structure. There are a number of different attributes that can provide that info:
If you were to explore the axes of the DataFrame, you may do so by having an array return the listed columns and index via DataFrame.columns and DataFrame.index. On the other hand, it may be useful to look at the different types of data that makes up the dataset. In these situations, the DataFrame.dtypes is used.
Manipulating a DataFrameNow that we know what DataFrame is, it’s time to do some real work! Principally, this involves manipulating it as part of the data cleaning and data wrangling process, just prior to the actual analysis. Now there are a number of basic operations that should be in everyone’s repertoire — the first one is being able to access and isolate a given segment of a DataFrame. To segment a DataFrame, we use either the DataFrame.loc attribute or DataFrame.iloc attribute where the input dictates which rows or columns are extracted ([rows: columns]). If a column needs to be isolated, then the process would be to use square brackets with the name of the given column.
In the event that we need to extract multiple rows or columns, we use the slice method, which involves using a “:” that indicates a continuous range with the end range being exclusive (i.e., not included) or by inputting criteria within square brackets in a similar manner as indexing with Boolean with NumPy.
While the above examples are simplistic, it’s possible to make it more powerful and sophisticated with the use of operators such as AND (&), OR (|), NOT EQUAL TO (!=) or EQUAL TO (==). To set this up, let’s create a new DataFrame containing information about current UFC champions:
Aside from filtering out a DataFrame or segmenting it, it’s also possible to use the
In some situations, it may be necessary to insert or delete data from a DataFrame. We can insert or delete a row using the Recall that we created a DataFrame consisting of UFC champions that held the title at the end of 2021 with their monikers and win-loss record:
In the case for adding a column, the process would be similar to that of adding an item into a dictionary.
Sometimes with datasets, the labels used in identifying a column may not accurately describe its property. To change these labels, we can use the
Lastly, there may be some cases when we need to reshape the current makeup of the dataset to make it suitable for data analysis. While it’s certainly possible to manually remake another DataFrame, it’ll be easier to transform it. In Pandas, there are three different transformation functions that we can use to reshape the DataFrame: Method 1 — PivotingThis transformation is essentially taking a longer-format DataFrame and making it broader. Often this is a result of having a unique identifier repeated along multiple rows for each subsequent entry. One method to derive a newly formatted DataFrame is by using DataFrame.pivot. This method requires defining which of the data columns will be used as the new index and index as well as values for the DataFrame.
The
Method 2 — Stacking/UnstackingSometimes a DataFrame may have multiple indices that’ll look something like this:
It’s often difficult to make sense of the data or address it for analysis. So, functions such as
Method 3 — MeltingKnown as unpivoting a DataFrame, this works by essentially converting a wide-format DataFrame to a long-format. This usually occurs when more than one column works as an identifier for a given analysis. In order to transform the DataFrame to a longer format, we’ll need to use the
So far, we’ve only scratched the surface of DataFrames. There are many more functions and methods that can operate on these data structures within Python to gain deeper insights into your data. You can find these in the Pandas DataFrame reference guide. However, a great place to start is with the Pandas and NumPy Fundamentals course on Dataquest. Once you know the fundamentals, progress to working with data in Python in some of the other courses in the Data Analyst career path. How do you populate a DataFrame?Fill Data in an Empty Pandas DataFrame by Appending Rows
First, create an empty DataFrame with column names and then append rows one by one. The append() method can also append rows. When creating an empty DataFrame with column names and row indices, we can fill data in rows using the loc() method.
How do you add data to a DataFrame in python?append() function is used to append rows of other dataframe to the end of the given dataframe, returning a new dataframe object. Columns not in the original dataframes are added as new columns and the new cells are populated with NaN value.
How do I populate a row in pandas?How to fill a Pandas DataFrame row by row in Python. df = pd. DataFrame(columns=('A', 'B')) for i in range(3): df. loc[i] = [i, i + 1] ... . df = pd. DataFrame(columns=['A', 'B']) values_to_add = {'A': 1, 'B': 2} row_to_add = pd. ... . all_rows = [[1,2,3], [4,5,6]] df = pd. DataFrame(all_rows, columns=['A', 'B', 'C']) print(df). What is ILOC () in python?The iloc() function in python is one of the functions defined in the Pandas module that helps us to select a specific row or column from the data set. Using the iloc() function in python, we can easily retrieve any particular value from a row or column using index values.
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