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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 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
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 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