Python Articles - Page 350 of 829

Let’s say the following are the contents of our CSV file −       Car Reg_Price 0      BMW 2000 1    Lexus 1500 2     Audi 1500 3   Jaguar 2000 4  Mustang 1500Import the required libraries −import pandas as pd import matplotlib.pyplot as mpOur CSV file is on the Desktop. Load data from a CSV file into a Pandas ... Read More

To plot multiple columns, we will be plotting a Bar Graph. Use the plot() method and set the kind parameter to bar for Bar Graph. 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 multiple columns in a bar graph. We have set the “kind” parameter as “bar” for this −dataFrame.plot(x="Team", y=["Rank_Points", "Year" ], kind="bar", figsize=(10, ... 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. Plotting horizontal bar plots with dataset columns as x and y values. Use the estimator parameter to set median as the estimate of central tendency.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 plt from numpy import medianLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers2.csv") Plotting horizontal bar plots with ... Read More

Append a list of dictionaries to an existing Pandas DataFrame, use the append() method. At first, create a DataFrame −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Audi', 'XUV', 'Lexus', 'Volkswagen'], "Place": ['Delhi', 'Bangalore', 'Pune', 'Chandigarh', 'Mumbai'], "Units": [100, 150, 50, 110, 90] } ) Create list of Dictionaries − d = [{'Car': 'Mustang', 'Place': 'Hyderabad', 'Units': 60}, {'Car': 'Tesla', 'Place': 'Kerala', 'Units': 30}, {'Car': 'RollsRoyce', 'Place': 'Punjab', 'Units': 70}, {'Car': 'Bentley', 'Place': 'Gujarat', 'Units': 80}     ] Now, append list of Dictionaries to an already created DataFrame −dataFrame = dataFrame.append(d, ignore_index=True, ... Read More

Use the colDrop() method of pdpipe library to remove a column from Pandas DataFrame. At first, import the required pdpipe and pandas libraries with their respective aliases −import pdpipe as pdp import pandas as pdLet us create a DataFrame. Here, we have two columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )To remove a column from the DataFrame, use the ColDrop() method. Here, we are removing the “Units” column −resDF = pdp.ColDrop("Units").apply(dataFrame) ExampleFollowing is the complete code − import pdpipe as ... Read More

To compute first of group values, use the groupby.first() method. At first, import the required library with an alias −import pandas as pd;Create a DataFrame with 3 columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'BMW', 'Tesla', 'Lexus', 'Tesla'], "Place": ['Delhi', 'Bangalore', 'Pune', 'Punjab', 'Chandigarh', 'Mumbai'], "Units": [100, 150, 50, 80, 110, 90] } )Now, group DataFrame by a column −groupDF = dataFrame.groupby("Car") Compute first of group values and resetting index −res = groupDF.first() res = res.reset_index()ExampleFollowing is the complete code − import pandas as pd; dataFrame = pd.DataFrame(    {   ... Read More

To extract the value names and counts, let us first create a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'], "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000], "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500], "Units Sold": [ 200, 120, 150, 120, 210, 250, 220] })Fetch the value names and count for a specific column Car −res = dataFrame['Car'].value_counts() Fetch the value names and count for a specific column Units Sold −res = dataFrame['Units Sold'].value_counts()ExampleFollowing is the complete code −import pandas as pd # creating dataframe dataFrame = pd.DataFrame({"Car": ['BMW', ... Read More

To merge Pandas DataFrame, use the merge() function. The cartesian product is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “cross”At first, let us import the pandas library with an alias −import pandas as pd Create DataFrame1 −dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 120] } )Create DataFrame2dataFrame2 = pd.DataFrame(    {       "Car": ['BMW', 'Tesla', 'Jaguar'], "Reg_Price": [7000, 8000, 9000] } )Next, merge DataFrames with "cross" in "how" parameter i.e. ... Read More

The notnull() method returns a Boolean value i.e. if the DataFrame is having null value(s), then False is returned, else True.Let’s say the following is our CSV file with some NaN i.e. null values −Let us first read the CSV file −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")Checking for not null values −res = dataFrame.notnull()Now, on displaying the DataFrame, the CSV data will be displayed in the form of True and False i.e. boolean values because notnull() returns boolean. For Null values, False will get displayed. For Not-Null values, True will get displayed.ExampleFollowing is the complete code −import pandas as pd # reading ... Read More

To drop the null rows in a Pandas DataFrame, use the dropna() method. Let’s say the following is our CSV file with some NaN i.e. null values −Let us read the CSV file using read_csv(). Our CSV is on the Desktop −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")Remove the null values using dropna() −dataFrame = dataFrame.dropna() ExampleFollowing is the complete code −import pandas as pd # reading csv file dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv") print("DataFrame...", dataFrame) # count the rows and columns in a DataFrame print("Number of rows and column in our DataFrame = ", dataFrame.shape) dataFrame = dataFrame.dropna() print("DataFrame after removing null ... Read More