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In this task, we will find the length of strings in a Pandas series. We will use the str.len() function in the Pandas library for this purpose.AlgorithmStep 1: Define a Pandas series of string. Step 2: Find the length of each string using the str.len() function. Step 3: Print the results.Example Codeimport pandas as pd series = pd.Series(["Foo", "bar", "London", "Quarantine"]) print("Series: ", series) length = series.str.len() print("Length:", length)OutputSeries: 0 Foo 1 bar 2 London 3 Quarantine dtype: object Length: 0 3 1 3 2 6 3 10 dtype: int64
First, we can get the axes. Then, ax.spines could help to set the color by specifying the name of the axes, i.e., top, bottom, right and left.StepsAdd an axes to the current figure and make it the current axes.Using step 1 axes, we can set the color of all the axes.Using ax.spines[axes].set_color(‘color’), set the color of the axes. Axes could be bottom, top, right, and left. Color could be yellow, red, black, and blue.To show the figure, use the plt.show() method.Examplefrom matplotlib import pyplot as plt ax = plt.axes() ax.spines['bottom'].set_color('yellow') ax.spines['top'].set_color('red') ax.spines['right'].set_color('black') ax.spines['left'].set_color('blue') plt.show()OutputRead More
To divide each column by a particular column, we can use division sign (/). For example, if we have a data frame called df that contains three columns say x, y, and z then we can divide all the columns by column z using the command df/df[,3].ExampleConsider the below data frame − Live Demox1
In this program, we will convert a date string like "24 August 2020" to 2020-08-24 00:00:00. We will use the to_datetime() function in pandas library to solve this task.AlgorithmStep 1: Define a Pandas series containing date string. Step 2: Convert these date strings into date time format using the to_datetime format(). Step 3: Print the results.Example Codeimport pandas as pd series = pd.Series(["24 August 2020", "25 December 2020 20:05"]) print("Series: ", series) datetime = pd.to_datetime(series) print("DateTime Format: ", datetime)OutputSeries: 0 24 August 2020 1 25 December 2020 20:05 dtype: object DateTime Format: 0 2020-08-24 00:00:00 1 2020-12-25 20:05:00 dtype: datetime64[ns]
Using plt.rcParams["figure.figsize"], we can get the width setting.StepsTo get the plot width setting, use plt.rcParams["figure.figsize"] statement.Override the plt.rcParams["figure.figsize"] with a tuple (12, 9).After updating the width, get the updated width using plt.rcParams["figure.figsize"].ExamplesIn IDEExampleimport matplotlib.pyplot as plt print("Before, plot width setting:", plt.rcParams["figure.figsize"]) plt.rcParams["figure.figsize"] = (12, 9) print("Before, plot width setting:", plt.rcParams["figure.figsize"])OutputBefore, plot width setting: [6.4, 4.8] Before, plot width setting: [12.0, 9.0]In IPythonExampleIn [1]: from matplotlib import pyplot as plt In [2]: plt.rcParams["figure.figsize"]OutputOut[2]: [6.4, 4.8]
A regular expression is a sequence of characters that define a search pattern. In this program, we will use these regular expressions to filter valid and invalid emails.We will define a Pandas series with different emails and check which email is valid. We will also use a python library called re which is used for regex purposes.AlgorithmStep 1: Define a Pandas series of different email ids. Step 2: Define a regex for checking validity of emails. Step 3: Use the re.search() function in the re library for checking the validity of the email.Example Codeimport pandas as pd import re ... Read More
To calculate the z score for grouped data, we can use ave function and scale function. For example, if we have a data frame called df that contains a grouping coloumn say GROUP and a numerical column say Response then we can use the below command to calculate the z score for this data −ave(df$Response,df$GROUP,FUN=scale)ExampleConsider the below data frame − Live Demogrp
We can first create bars and then, by using set_color, we can set the color of the bars.StepsPass two lists consisting of four elements, into the bars method argument.Step 1 returns bars.Return values (0, 1, 2, 3) can be set with different colors, using set_color() method. Green, Black, Red color will be set and one bar will have the default color.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt bars = plt.bar([1, 2, 3, 4], [1, 2, 3, 4]) bars[0].set_color('green') bars[1].set_color('black') bars[2].set_color('red') plt.show()Output
A percentile is a term used in statistics to express how a score compares to other scores in the same set. In this program, we have to find nth percentile of a Pandas series.AlgorithmStep 1: Define a Pandas series. Step 2: Input percentile value. Step 3: Calculate the percentile. Step 4: Print the percentile.Example Codeimport pandas as pd series = pd.Series([10, 20, 30, 40, 50]) print("Series:", series) n = int(input("Enter the percentile you want to calculate: ")) n = n/100 percentile = series.quantile(n) print("The {} percentile of the given series is: {}".format(n*100, percentile))OutputSeries: 0 10 1 ... Read More
In this program, we will calculate the frequency of each element in a Pandas series. The function value_counts() in the pandas library helps us to find the frequency of elements.AlgorithmStep 1: Define a Pandas series. Step 2: Print the frequency of each item using the value_counts() function.Example Codeimport pandas as pd series = pd.Series([10,10,20,30,40,30,50,10,60,50,50]) print("Series:", series) frequency = series.value_counts() print("Frequency of elements:", frequency)OutputSeries: 0 10 1 10 2 20 3 30 4 40 5 30 6 50 7 10 8 60 9 50 10 50 dtype: int64 Frequency of elements: 50 3 10 3 30 2 20 1 40 1 60 1 dtype: int64