Horizontal trend is also called Stationery trend. Let’s say the following is our dataset i.e. SalesRecords3.csvAt first, import the required libraries −import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords3.csv") Casting column to datetime object −dataFrame['Sold_On'] = pd.to_datetime(dataFrame['Sold_On'])Create the plot for downtrend −dataFrame.plot() ExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as plt # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords3.csv") print("Reading the CSV file...", dataFrame) # casting column to datetime object dataFrame['Sold_On'] = pd.to_datetime(dataFrame['Sold_On']) dataFrame = dataFrame.set_index('Sold_On') ... Read More

Violin Plot in Seaborn is used to draw a combination of boxplot and kernel density estimate. The seaborn.violinplot() is used for this.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required 3 libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") Plotting violin plot with column Weight (kgs) −sb.violinplot(dataFrame['Weight'])ExampleLet us see an example −import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # Load data from a CSV file into ... Read More

For a stacked Horizontal Bar Chart, create a Bar Chart using the barh() and set the parameter “stacked” as True −Stacked = TrueAt first, import the required libraries −import pandas as pd import matplotlib.pyplot as pltCreate a DataFrame with 3 columns −dataFrame = pd.DataFrame({"Car": ['Bentley', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], })Plotting stacked Horizontal Bar Chart with all the columns −dataFrame.plot.barh(stacked=True, title='Car Specifications', color=("orange", "cyan")) ExampleFollowing is the complete code −import pandas as pd import matplotlib.pyplot as plt # creating dataframe dataFrame = pd.DataFrame({"Car": ['Bentley', 'Lexus', ... Read More

The eval() function can also be used to evaluate the sum of rows with the specified columns. At first, let us create a DataFrame with Product records −dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Finding sum using eval(). The resultant column with the sum is also mentioned in the eval(). The expression displays the sum formulae assigned to the resultant column −dataFrame = dataFrame.eval('Result_Sum = Opening_Stock + Closing_Stock')ExampleFollowing is the complete code −import pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, ... Read More

Box Plot in Seaborn is used to draw a box plot to show distributions with respect to categories. The seaborn.boxplot() is used for this.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required 3 libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") ExampleFollowing is the code −import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # Load data from a CSV file into a Pandas DataFrame: dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") ... Read More

SactterPlot in Seaborn is used to draw a scatter plot with possibility of several semantic groupings. The seaborn.scatterplot() is used for this.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required 3 libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") Plotting scatterplot with Age and Weight (kgs). The hue parameter set as "Role" −sb.scatterplot(dataFrame['Age'], dataFrame['Weight'], hue=dataFrame['Role'])ExampleFollowing is the code −import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # ... Read More

Lineplot in Seaborn is used to draw a line plot with possibility of several semantic groupings. The seaborn.lineplot() is used for this.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required 3 libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") ExampleFollowing is the code − import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") print("Reading ... Read More

Bar Plot in Seaborn is used to show point estimates and confidence intervals as rectangular bars. The seaborn.barplot() is used. Control ordering by passing an explicit order i.e. ordering on the basis of a specific column using the order parameter.Let’s say the following is our dataset in the form of a CSV file −Cricketers2.csvAt first, import the required libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers2.csv") Plotting horizontal bar plots with Matches and Academy columns. Control order by passing an explicit order i.e. ... Read More

To create a Time Series Plot, use the lineplot(). At first, import the required libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltCreate a DataFrame with one of the columns as date i.e. “Date_of_Purchase” −dataFrame = pd.DataFrame({'Date_of_Purchase': ['2018-07-25', '2018-10-25', '2019-01-25', '2019-05-25', '2019-08-25', '2020-09-25', '2021-03-25'], 'Units Sold': [98, 77, 45, 70, 70, 87, 66] })Pot Time Series using lineplot() −sb.lineplot(x="Date_of_Purchase", y="Units Sold", data=dataFrame) ExampleFollowing is the code −import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # creating DataFrame dataFrame = pd.DataFrame({'Date_of_Purchase': ['2018-07-25', '2018-10-25', '2019-01-25', '2019-05-25', '2019-08-25', '2020-09-25', '2021-03-25'], 'Units Sold': [98, 77, ... Read More

To count the NaN occurrences in a column, use the isna(). Use the sum() to add the values and find the count.At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame. We have set the NaN values using the Numpy np.inf in “Units_Sold” column −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, np.NaN, 150, np.NaN, 200, np.NaN] })Count NaN values from column "Units_Sold" −dataFrame["Units_Sold"].isna().sum() ExampleFollowing is the code −import pandas ... Read More