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To find numeric columns in Pandas, we can make a list of integers and then include it into select_dtypes() method. Let's take an example and see how to apply this method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Make a list of data type, i.e., numerics, to select a column.Return a subset of the DataFrame's columns based on the column dtypes.Print the column whose data type is int.Example import pandas as pd df = pd.DataFrame( dict( name=['John', 'Jacob', 'Tom', 'Tim', 'Ally'], ... Read More
To query the columns of a Pandas DataFrame, use the query(). We are querying to filter records. At first, let us create a Pandas DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Using query() to query columns with conditions −print(dataFrame.query('Opening_Stock >=500 & Closing_Stock < 1000 & Product.str.startswith("P").values'))ExampleFollowing is the complete code −import pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame # using query() to query columns print"Querying columns to filter records..." print(dataFrame.query('Opening_Stock >=500 & Closing_Stock ... Read More
To select multiple rows from a DataFrame, set the range using the : operator. At first, import the require pandas library with alias −import pandas as pdNow, create a new Pandas DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Select multiple rows using the : operator −dataFrame[0:2]ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b']) # DataFrame print"DataFrame...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['z']) ... Read More
To select a column from a DataFrame, just fetch it using square brackets. Mention the column to select in the brackets and that’s it, for exampledataFrame[‘ColumnName’]At first, import the required library −import pandas as pdNow, create a DataFrame. We have two columns in it −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )To select only a single column, mention the column name using the square bracket as shown below. Here, our ... Read More
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