Swarm Plot in Seaborn is used to draw a categorical scatterplot with non-overlapping points. The seaborn.swarmplot() is used for this. 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 swarm plot with the “Matches” column −sb.swarmplot(x = dataFrame["Matches"])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 ... 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. Observations show as a stick using the inner parameter with value stick.Let’s say the following is our dataset in the form of a CSV file − Cricketers.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\Cricketers.csv") Plotting violin plot with Academy and Age. Control order by passing an explicit order i.e. ordering on the basis of "Academy". Observations ... Read More

Let’s say the following is our CSV file −SalesRecords.csvAnd we need to generate 3 excel files from the above existing CSV file. The 3 CSV files should be on the basis of the Car names i.e. BMW.csv, Lexus.csv and Jaguar.csv.At first, read our input CSV file i.e. SalesRecord.csv −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords.csv")Use groupby() to generate CSVs on the basis of Car names in Car column −for (car), group in dataFrame.groupby(['Car']): group.to_csv(f'{car}.csv', index=False)ExampleFollowing is the code −import pandas as pd # DataFrame to read our input CS file dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords.csv") print("Input CSV file = ", dataFrame) ... Read More

Suppose we have an array called nums, we have to find the maximum length of a subarray where the product of all its elements is positive. We have to find the maximum length of a subarray with positive product.So, if the input is like nums = [2, -2, -4, 5, -3], then the output will be 4 because first four elements are forming a subarray whose product is positive.To solve this, we will follow these steps :Define a function util() . This will take s, eneg := 0ns := -1, ne := -1for i in range s to e, doif ... Read More

To write pandas dataframe to a CSV file in Python, use the to_csv() method. At first, let us create a dictionary of lists −# dictionary of lists d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_purchase': ['2020-10-10', '2020-10-12', '2020-10-17', '2020-10-16', '2020-10-19', '2020-10-22'] }Now, create pandas dataframe from the above dictionary of lists −dataFrame = pd.DataFrame(d) Our output CSV file will generate on the Desktop since we have set the Desktop path below −dataFrame.to_csv("C:\Users\amit_\Desktop\sales1.csv\SalesRecords.csv")ExampleFollowing is the code −import pandas as pd # dictionary of lists d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_purchase': ['2020-10-10', '2020-10-12', '2020-10-17', '2020-10-16', ... Read More

To merge all CSV files, use the GLOB module. The os.path.join() method is used inside the concat() to merge the CSV files together. Some of the common methods we can use to merge multiple CSV Files into a single dataframe are as follows - os.path.join() and glob Merging CSV Files with glob Pattern ... Read More

Suppose we have an array containing various integer values and a given length k. We have to find out the greatest subarray from the array of the given length. A subarray is said to be greater than another subarray, if subarray1[i] ≠ subarry2[i] and subarray1[i] > subarry2[i].So, if the input is like nums = [5, 3, 7, 9], k = 2, then the output will be [7, 9].To solve this, we will follow these steps −start := size of nums - kmax_element := nums[start]max_index := startwhile start >= 0, doif nums[start] > max_element is non-zero, thenmax_element := nums[start]max_index := startreturn ... 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. Plot a sinle violinplot usinga single column.Let’s say the following is our dataset in the form of a CSV file − Cricketers.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\Cricketers.csv") Plotting single horizontal violin plot with Weight (kgs) column −sb.violinplot(dataFrame['Weight'])ExampleFollowing is the code −import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # ... Read More

In Python, to sort a CSV file by multiple columns, we can use the 'sort_values()' Method provided by the Python Pandas library. This method is used for sorting the values by taking column names as arguments. Some of the common methods for sorting a CSV file by multiple columns are as follows. sort_values() : To sort a DataFrame by multiple columns. sort_values() without inplace : To sort a DataFrame by ... Read More

Count Plot in Seaborn is used to display the counts of observations in each categorical bin using bars. The seaborn.countplot() is used for this. Style the bars using the facecolor, linewidth and edgecolor parameters.Let’s say the following is our dataset in the form of a CSV file − Cricketers.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\Cricketers.csv") Style and design the bars using the facecolor, linewidth and edgecolor parameters −sb.countplot(dataFrame["Age"], facecolor=(0, 0.0, 0, 0), linewidth=3, edgecolor=sb.color_palette("dark", 2))ExampleFollowing is the ... Read More