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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
The numpy where() method can be used to filter Pandas DataFrame. Mention the conditions in the where() method. At first, let us import the required libraries with their respective aliasimport pandas as pd import numpy as npWe will now create a Pandas DataFrame with Product records dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Use numpy where() to filter DataFrame with 2 ConditionsresValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000)) print"Filtered DataFrame Value = ", dataFrame.loc[resValues1] Let us use numpy where() again to filter DataFrame with 3 conditionsresValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) ... Read More
It's quite simple to rename a DataFrame column name in Pandas. All that you need to do is to use the rename() method and pass the column name that you want to change and the new column name. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use rename() method to rename the column name. Here, we will rename the column "x" with its new name "new_x".Print the DataFrame with the renamed column.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, ... Read More
To append rows to a DataFrame, use the append() method. Here, we will create two DataFrames and append one after the another.At first, import the pandas library with an alias −import pandas as pdNow, create the 1st DataFramedataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'] } )Create the 2nd DataFramedataFrame2 = pd.DataFrame( { "Car": ['Mercedes', 'Tesla', 'Bentley', 'Mustang'] } )Next, append rows to the enddataFrame1 = dataFrame1.append(dataFrame2)ExampleFollowing is the codeimport pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'] } ) print"DataFrame1 ...", dataFrame1 # Find ... Read More
To access a group of rows in a Pandas DataFrame, we can use the loc() method. For example, if we use df.loc[2:5], then it will select all the rows from 2 to 5.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use df.loc[2:5] to select the rows from 2 to 5.Print the DataFrame.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], "y": [4, 7, 5, 1, 5, 1, 4, 7], "z": [9, 3, 5, 1, 5, 1, 9, 3] } ) print "Input DataFrame is:", df df = df.loc[2:5] print "New DataFrame:", dfOutput Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3 New DataFrame: x y z 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1
To create a subset of DataFrame by column name, use the square brackets. Use the DataFrame with square brackets (indexing operator) and the specific column name like this −dataFrame[‘column_name’]At first, import the required library with alias −import pandas as pdCreate a Pandas DataFrame with Product records −dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Let us fetch a subset i.e. we are fetching only Product column recordsdataFrame['Product']ExampleFollowing is the codeimport pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) ... Read More
To delete the first three rows of a DataFrame in Pandas, we can use the iloc() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Delete the first three rows using df.iloc[3:].Print the updated DataFrame.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], "y": [4, 7, 5, 1, 5, 1, 4, 7], "z": [9, 3, 5, 1, 5, 1, 9, 3] } ) print "Input DataFrame is:", df df = df.iloc[3:] print "After deleting the first 3 rows: ", dfOutput Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3 After deleting the first 3 rows: x y z 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3