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To merge Pandas DataFrame, use the merge() function. The outer join is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “outer”At first, let us import the pandas library with an alias −import pandas as pdLet us create DataFrame1 −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Let us now create DataFrame2 −dataFrame2 = pd.DataFrame( { ... Read More
To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name. To set NaN for unmatched values, use the “how” parameter and set it left or right. That would mean, merging left or right.At first, let us import the pandas library with an alias −import pandas as pdLet us create DataFrame1 −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Let us ... Read More
To group Pandas dataframe, we use groupby(). To sort grouped dataframe in ascending order, use sort_values(). The size() method is used to get the dataframe size.For ascending order sort, use the following in sort_values() −ascending=TrueAt first, create a pandas dataframe −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], "Reg_Price": [1000, 1400, 1000, 900, 1700, 900] } )Next, group according to Reg_Price column and sort in ascending order −dataFrame.groupby('Reg_Price').size().sort_values(ascending=True)ExampleFollowing is the code −import pandas as pd # dataframe ... Read More
To filter few rows from DataFrame on the basis of sum, we have considered an example with Student Marks. We need to calculate the sum of a particular subject wherein the total is more than 200 i.e. the total of all 3 students in that particular subject is more than 200. In this way we can fiter our rows with total less than 200.At first, let us create a DataFrame with 3 columns i.e. records of 3 students −dataFrame = pd.DataFrame({'Jacob_Marks': [95, 90, 70, 85, 88], 'Ted_Marks': [60, 50, 65, 85, 70], 'Jamie_Marks': [77, 76, 60, 45, 50]})Filtering on the ... Read More
To fetch the common rows between two DataFrames, use the concat() function. Let us create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1000, 1500, 1100, 800, 1100, 900] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1200, 1500, 1000, 800, 1100, 1000] } )Finding common rows between two DataFrames with concat() −dfRes = pd.concat([dataFrame1, dataFrame2])Reset index −dfRes = dfRes.reset_index(drop=True)Groupby columns −dfGroup = dfRes.groupby(list(dfRes.columns))Getting the length of each row to calculate the count. If ... Read More
To check if the DataFrame objects are equal, use the equals() method. At first, let us create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] } )Create DataFrame2 with two columns dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] } )To check if the DataFrame ... Read More
To concatenate more than two Pandas DataFrames, use the concat() method. Set the axis parameter as axis = 0 to concatenate along rows. At first, import the required library −import pandas as pdLet us create the 1st DataFrame −dataFrame1 = pd.DataFrame( { "Col1": [10, 20, 30], "Col2": [40, 50, 60], "Col3": [70, 80, 90], }, index=[0, 1, 2], ) Let us create the 2nd DataFrame −dataFrame2 = pd.DataFrame( { "Col1": [100, 110, 120], "Col2": [130, 140, 150], "Col3": [160, 170, 180], }, ... Read More
To concatenate more than two Pandas DataFrames, use the concat() method. Set the axis parameter as axis = 1 to concatenate along columns. At first, import the required library −import pandas as pdLet us create the 1st DataFrame −dataFrame1 = pd.DataFrame( { "Col1": [10, 20, 30], "Col2": [40, 50, 60], "Col3": [70, 80, 90], }, index=[0, 1, 2], )Let us create the 2nd DataFrame −dataFrame2 = pd.DataFrame( { "Col1": [100, 110, 120], "Col2": [130, 140, 150], "Col3": [160, 170, 180], }, ... Read More
A regular expression (regex) is a sequence of characters that define a search pattern. To filter rows in Pandas by regex, we can use the str.match() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable regex for the expression. Supply a string value as regex, for example, the string 'J.*' will filter all the entries that start with the letter 'J'.Use df.column_name.str.match(regex) to filter all the entries in the given column name by the supplied regex.Example import pandas as pd df = pd.DataFrame( dict( name=['John', 'Jacob', 'Tom', 'Tim', 'Ally'], ... Read More
The indexing operator is the square brackets for creating a subset dataframe. Let us first create a Pandas DataFrame. We have 3 columns in the DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Creating a subset with a single columndataFrame[['Product']]Creating a subset with multiple columnsdataFrame[['Opening_Stock', 'Closing_Stock']]ExampleFollowing is the complete codeimport pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame print"Displaying a subset using indexing operator:", dataFrame[['Product']] print"Displaying a subset with multiple columns:", dataFrame[['Opening_Stock', 'Closing_Stock']]OutputThis will ... Read More