To replace NaN values, use the fillna() method. Let’s say the following is our CSV file opened in Microsoft Excel with some NaN values −At first, import the required library −import pandas as pdLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")Replace NaN values with 0s using the fillna() method −dataFrame.fillna(0)ExampleFollowing is the codeimport pandas as pd # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv") print("DataFrame...", dataFrame) # replace NaN values with 0s res = dataFrame.fillna(0) print("DataFrame after replacing NaN values...", res)OutputThis will produce the following output −DataFrame... ... Read More

Bar Plot in Seaborn is used to show point estimates and confidence intervals as rectangular bars. The seaborn.barplot() is used for this. Plot vertical bar plots grouped by a categorical variable, by passing the variable as x or y coordinates in the barplot() method.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 vertical bar plots grouped by a categorical variable −sb.barplot(x = dataFrame["Role"], y ... Read More

Swarm Plot in Seaborn is used to draw a categorical scatterplot with non-overlapping points. The seaborn.swarmplot() is used for this. Draw swarms of observations on top of a violin plot using the violinplot().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")Draw swarms of observations on top of a violin plot −sb.violinplot(x = dataFrame["Role"], y = dataFrame["Matches"]) sb.swarmplot(x = dataFrame["Role"], y = dataFrame["Matches"], color="white")ExampleFollowing is ... Read More

To read CSV file without header, use the header parameter and set it to “None” in the read_csv() method.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, import the required library −import pandas as pdLoad data from a CSV file into a Pandas DataFrame. This will display the headers as well −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")While loading, use the header parameter and set None to load the CSV without header −pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv", header=None)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") ... Read More

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 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