To apply the aggregation list, use the agg() method. At first, import the required library −import pandas as pdCreate a DataFrame with two columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'], "Units": [100, 150, 110, 80, 110, 90] } ... Read More

Point Plot in Seaborn is used to show point estimates and confidence intervals using scatter plot glyphs. The seaborn.pointplot() is used for this. For vertical point plot grouped by a categorical variable, set the variable as a value for the pointplot().Let’s say the following is our dataset in the form ... Read More

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 ... 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 ... 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 ... 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 ... 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 ... 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 ... 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 ... 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 ... Read More