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
Python Articles
Page 750 of 855
Python xticks in subplots
Subplot can split the figure in nrow*ncols parts and plt.xticks could help to plot the xticks for subplots.StepsCreate two lists for line 1 and line 2.Add a subplot to the current figure, nrow = 1, ncols = 2 and index = 1.Draw line 1 with style as dashed.Set or retrieve auto scaling margins(0.2).Place xticks at even places.Set a title for the X-axis.Add a subplot to the current figure, nrow = 1, ncols = 2 and index = 2.Plot line 2.Set or retrieve auto scaling margins(0.2).Place xticks at odd places.Set a title for the X-axis.To show the figure use plt.show() method.Exampleimport ...
Read MoreComparing two Pandas series and printing the the difference
In this program, we will compare two Pandas series and will print the differences in the series. By difference, we mean that the index positions at which the elements did not match.AlgorithmStep 1: Define two Pandas series, s1 and s2. Step 2: Compare the series using compare() function in the Pandas series. Step 3: Print their difference.Example Codeimport pandas as pd s1 = pd.Series([10, 20, 30, 40, 50, 60]) s2 = pd.Series([10, 30, 30, 40, 55, 60]) print("S1:", s1) print("S2:", s2) difference = s1.compare(s2) print("Difference between the series: ", difference)OutputS1: 0 10 1 20 2 ...
Read MorePrint the standard deviation of Pandas series
In this program, we will find the standard deviation of a Pandas series. Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance.AlgorithmStep 1: Define a Pandas series Step 2: Calculate the standard deviation of the series using the std() function in the pandas library. Step 3: Print the standard deviation.Example Codeimport pandas as pd series = pd.Series([10,20,30,40,50]) print("Series: ", series) series_std = series.std() print("Standard Deviation of the series: ",series.std())OutputSeries: 0 10 1 20 2 30 3 40 4 50 dtype: int64 Standard Deviation of the series: 15.811388300841896
Read MorePrint the mean of a Pandas series
mean() function in the Pandas library can be used to find the mean of a series.AlgorithmStep 1: Define a Pandas series. Step 2: Use the mean() function to calculate the mean. Step 3: Print the mean.Example Codeimport pandas as pd series = pd.Series([10,20,30,40,50]) print("Pandas Series: ", series) series_mean = series.mean() print("Mean of the Pandas series:", series_mean)OutputPandas Series: 0 10 1 20 2 30 3 40 4 50 dtype: int64 Mean of the Pandas series: 30.0
Read MorePandas timeseries plot setting X-axis major and minor ticks and labels
Using Pandas, we can create a dataframe with time and speed, and thereafter, we can use the data frame to get the desired plot.StepsConstruct a new Generator with the default BitGenerator (PCG64).Using Pandas, get a fixed frequency DatetimeIndex. From '2020-01-01' to '2021-01-01'.Draw samples from a log-normal distribution.Make a data frame with above data.Using panda dataframe create plot, with figsize = (10, 5).To show the figure, use the plt.show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt rng = np.random.default_rng(seed=1) date_day = pd.date_range(start='2020-01-01', end='2021-01-01', freq='D') traffic = rng.lognormal(sigma=2, size=date_day.size) df_day = pd.DataFrame(dict(speed=[pow(2, -i) for ...
Read MoreHow to append elements to a Pandas series?
In this program, we will append elements to a Pandas series. We will use the append() function for this task. Please note that we can only append a series or list/tuple of series to the existing series.AlgorithmStep1: Define a Pandas series, s1. Step 2: Define another series, s2. Step 3: Append s2 to s1. Step 4: Print the final appended series.Example Codeimport pandas as pd s1 = pd.Series([10, 20, 30, 40, 50]) s2 = pd.Series([11, 22, 33, 44, 55]) print("S1:", s1) print("S2:", s2) appended_series = s1.append(s2) print("Final Series after appending:", appended_series)OutputS1: 0 10 1 20 ...
Read MoreHow to sort a Pandas Series?
In this problem we have to sort a Pandas series. We will define an unsorted pandas series and will sort it using the sort_values() function in the Pandas library.AlgorithmStep 1: Define Pandas series. Step 2: Sort the series using sort_values() function. Step 3: Print the sorted series.Example Codeimport pandas as pd panda_series = pd.Series([18, 15, 66, 92, 55, 989]) print("Unsorted Pandas Series: ", panda_series) panda_series_sorted = panda_series.sort_values(ascending = True) print("Sorted Pandas Series: ", panda_series_sorted)OutputUnsorted Pandas Series: 0 18 1 15 2 66 3 92 4 55 5 ...
Read MoreHow to print array elements within a given range using Numpy?
In this program, we have to print elements of a numpy array in a given range. The different numpy functions used are numpy.where() and numpy.logical_and().AlgorithmStep 1: Define a numpy array. Step 2: Use np.where() and np.logical_and() to find the numbers within the given range. Step 3: Print the result.Example Codeimport numpy as np arr = np.array([1,3,5,7,10,2,4,6,8,10,36]) print("Original Array:",arr) result = np.where(np.logical_and(arr>=4, arr
Read MoreHow to add a vector to a given Numpy array?
In this problem, we have to add a vector/array to a numpy array. We will define the numpy array as well as the vector and add them to get the result arrayAlgorithmStep 1: Define a numpy array. Step 2: Define a vector. Step 3: Create a result array same as the original array. Step 4: Add vector to each row of the original array. Step 5: Print the result array.Example Codeimport numpy as np original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) print("Original Array: ", original_array) vector = np.array([1, 1, 0]) print("Vector: ...
Read MoreHow to find the sum of rows and columns of a given matrix using Numpy?
In this problem, we will find the sum of all the rows and all the columns separately. We will use the sum() function for obtaining the sum.AlgorithmStep 1: Import numpy. Step 2: Create a numpy matrix of mxn dimension. Step 3: Obtain the sum of all the rows. Step 4: Obtain the sum of all the columns.Example Codeimport numpy as np a = np.matrix('10 20; 30 40') print("Our matrix: ", a) sum_of_rows = np.sum(a, axis = 0) print("Sum of all the rows: ", sum_of_rows) sum_of_cols = np.sum(a, axis = 1) print("Sum of all the columns: ", sum_of_cols)OutputOur ...
Read More