Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Articles by Rishikesh Kumar Rishi
Page 26 of 102
How do I put a circle with annotation in matplotlib?
To put a circle with annotation in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create data points using numpy.Get the point coordinate to put circle with annotation.Get the current axis.Plot the data and data points using plot() method.Set X and Y axes scale.To put a circled marker, use the plot() method with marker='o' and some properties.Annotate that circle (Step 7) with arrow style.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = np.array([[5, ...
Read MoreHow to shift a column in a Pandas DataFrame?
We can use the shift() method in Pandas to shift the columns of a DataFrame without having to rewrite the whole DataFrame. shift() takes the following parametersshift(self, periods=1, freq=None, axis=0, fill_value=None)periods Number of periods to shift. It can take a negative number too.axis It takes a Boolean value; 0 if you want to shift index and 1 if you want to shift columnfill_value It will replace the missing value.Let's take an example and see how to use this shift() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Select a column and shift it by using df["column_name]=df.column_name.shift()Print ...
Read MoreHow to find numeric columns in Pandas?
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 MoreHow to filter rows in Pandas by regex?
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 MorePython – Pandas Dataframe.rename()
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 MoreHow to access a group of rows in a Pandas DataFrame?
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
Read MoreDelete the first three rows of a DataFrame in Pandas
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
Read MoreHow to convert a DataFrame into a dictionary in Pandas?
To convert a Pandas DataFrame into a dictionary, we can use the to_dict() method. Let's take an example and see how it's done.StepsCreate two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Convert the DataFrame into a dictionary using to_dict() method and print it.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print "Input DataFrame is:", df print "Convert DataFrame into dictionary: ", df.to_dict()Output Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 Convert DataFrame into dictionary: {'x': {0: 5, 1: 2, 2: 7, 3: 0}, 'y': {0: 4, 1: 7, 2: 5, 3: 1}, 'z': {0: 9, 1: 3, 2: 5, 3: 1}}
Read MoreSelect DataFrame rows between two index values in Python Pandas
We can slice a Pandas DataFrame to select rows between two index values. 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.Initialize a variable for lower limit of the index.Initialize another variable for upper limit of the index.Use df[index_lower_limit: index_upper_limit] to print the DataFrame in range index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, ...
Read MoreSelecting with complex criteria from a Pandas DataFrame
We can use different criteria to compare all the column values of a Pandas DataFrame. We can perform comparison operations like df[col]2, then it will check all the values from col and compare whether they are greater than 2. For all the column values, it will return True if the condition holds, else False. 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.Initialize a variable col, with a column name.Perform some comparison operations.Print the resultant DataFrame.Example import pandas as pd df = pd.DataFrame( ...
Read More