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Scatter Plot is a data visualization technique. Use the plot.scatter() to plot the Scatter Plot. At first, Let us import the required libraries −We have our data with Team Records. Set it in the Pandas DataFrame −data = [["Australia", 2500], ["Bangladesh", 1000], ["England", 2000], ["India", 3000], ["Srilanka", 1500]] dataFrame = pd.DataFrame(data, columns=["Team", "Rank_Points"]) Let us plot now with the columns −dataFrame.plot.scatter(x="Team", y="Rank_Points")ExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as mp # our data data = [["Australia", 2500], ["Bangladesh", 1000], ["England", 2000], ["India", 3000], ["Srilanka", 1500]] # dataframe dataFrame = pd.DataFrame(data, columns=["Team", "Rank_Points"]) ... Read More
Using columns.values(), we can easily rename column name with index number of a CSV file.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −We will rename the column names. At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")Display all the column names from the CSV −dataFrame.columnsNow, rename column names −dataFrame.columns.values[0] = "Car Names" dataFrame.columns.values[1] = "Registration Cost" dataFrame.columns.values[2] = "Units Sold"ExampleFollowing is the code −import pandas as pd # Load data from a CSV file into a Pandas DataFrame: dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv") print("Reading the CSV file...", dataFrame) ... Read More
To select rows that contain specific text, use the contains() method. Let’s say the following is our CSV file path −C:\Users\amit_\Desktop\SalesRecords.csvAt first, let us read the CSV file and create Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")Now, let us select rows that contain specific text “BMW” −dataFrame = dataFrame[dataFrame['Car'].str.contains('BMW')]ExampleFollowing is the code −import pandas as pd # reading csv file dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv") print("DataFrame...", dataFrame) # select rows containing text "BMW" dataFrame = dataFrame[dataFrame['Car'].str.contains('BMW')] print("Fetching rows with text BMW ...", dataFrame)OutputThis will produce the following output −DataFrame ... Car Place UnitsSold ... Read More
To plot a density map in Python, we can take the following steps −Create side, x, y, and z using numpy. Numpy linspace helps to create data between two points based on a third number.Return coordinate matrices from coordinate vectors using side data.Create exponential data using x and y (Step 2).Create a pseudocolor plot with a non-regular rectangular grid using pcolormesh() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, cm, colors import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True side = np.linspace(-2, 2, 15) X, Y = np.meshgrid(side, side) Z = np.exp(-((X - 1) ... Read More
To merge Pandas DataFrame, use the merge() function. The many-to-one relation is implemented on both the DataFrames by setting under the “validate” parameter of the merge() function i.e. −validate = “many-to-one” or validate = “m:1”The many-to-one relation checks if merge keys are unique in right dataset.At first, let us create our 1st DataFrame −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 110, 80, 110, 90] } ) Now, let us create our 2nd DataFrame −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', ... Read More
Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")To select multiple column records, use the square brackets. Mention the columns in the brackets and fetch multiple columns from the entire dataset −dataFrame[['Reg_Price', 'Units']] ExampleFollowing is the code −import pandas as pd # Load data from a CSV file into a Pandas DataFrame: dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv") print("Reading the CSV file...", dataFrame) # displaying two columns res = dataFrame[['Reg_Price', 'Units']]; print("Displaying two columns : ", res)OutputThis will produce the ... Read More
To select a subset of rows, use conditions and fetch data.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")Let’s say we want the Car records with “Units” more than 100 i.e. subset of rows. For this, use −dataFrame[dataFrame["Units"] > 100] Now, let’s say we want the Car records with “Reg_Price” less than 100 i.e. subset of rows. For this, use −dataFrame[dataFrame["Reg_Price"] < 3000]ExampleFollowing is the code − import pandas as pd # Load data from a CSV file ... Read More
Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")To select a subset, use the square brackets. Mention the column in the brackets and fetch single column from the entire dataset −dataFrame['Car'] ExampleFollowing is the code − import pandas as pd # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv") print("Reading the CSV file...", dataFrame) # displaying only a single column res1 = dataFrame['Car']; # displaying only a subset print("Displaying only one column ... Read More
To plot a DataFrame in a Line Graph, use the plot() method and set the kind parameter to line. Let us first import the required libraries −import pandas as pd import matplotlib.pyplot as mpFollowing is our data with Team Records −data = [["Australia", 2500, 2021], ["Bangladesh", 1000, 2021], ["England", 2000, 2021], ["India", 3000, 2021], ["Srilanka", 1500, 2021]]Set the data as Pandas DataFrame and add columns −dataFrame = pd.DataFrame(data, columns=["Team", "Rank_Points", "Year"]) Plot the Pandas DataFrame in a line graph. We have set the “kind” parameter as “line” for this −dataFrame.plot(x="Team", y=["Rank_Points", "Year" ], kind="line", figsize=(10, 9))ExampleFollowing is the code −import ... Read More
To find unique values from multiple columns, use the unique() method. Let’s say you have Employee Records with “EmpName” and “Zone” in your Pandas DataFrame. The name and zone can get repeated since two employees can have similar names and a zone can have more than one employee. In that case, if you want unique Employee names, then use the unique() for DataFrame.At first, import the required library. Here, we have set pd as an alias −import pandas as pdAt first, create a DataFrame. Here, we have two columns −dataFrame = pd.DataFrame( { "EmpName": ['John', 'Ted', ... Read More