To plot multiple columns of Pandas DataFrame using Seaborn, we can take the following steps −Make a dataframe using Pandas.Plot a bar using Seaborn's barplot() method.Rotate the xticks label by 45 angle.To display the figure, use show() method.Exampleimport pandas import matplotlib.pylab as plt import seaborn as sns import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pandas.DataFrame({"X-Axis": [np.random.randint(10) for i in range(10)], "YAxis": [i for i in range(10)]}) bar_plot = sns.barplot(x='X-Axis', y='Y-Axis', data=df) plt.xticks(rotation=45) plt.show()Output

To annotate data points while plotting from pandas data frame, we can take the following steps −Create df using DataFrame with x, y and index keys.Create a figure and a set of subplots using subplots() method.Plot a series of data frame using plot() method, kind='scatter', ax=ax, c='red' and marker='x'.To annotate the scatter point with the index value, iterate the data frame.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt import string plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'x': np.random.rand(10), 'y': np.random.rand(10)}, index=list(string.ascii_lowercase[:10])) fig, ax = plt.subplots() df.plot('x', ... Read More

To set Y-Axis in matplotlib using Pandas, we can take the following steps −Create a dictionary with the keys, x and y.Create a data frame using Pandas.Plot data points using Pandas plot, with ylim(0, 25) and xlim(0, 15).To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True d = dict(    x=np.linspace(0, 10, 10),    y=np.linspace(0, 10, 10)*2 ) df = pd.DataFrame(d) df.plot(kind="bar", ylim=(0, 25), xlim=(0, 15)) plt.show()Output

To get rid of grid lines when plotting with Pandas with secondary_y, we can take the following steps −Create a data frame using DataFrame wth keys column1 and column2.Use data frame data to plot the data frame. To get rid of gridlines, use grid=False.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = pd.DataFrame({"column1": [4, 6, 7, 1, 8], "column2": [1, 5, 7, 8, 1]}) data.plot(secondary_y=[5], grid=False) plt.show()Output

To change order of plots in Pandas hist commad, we can take the following steps −Make a data frame using Pandas.Plot a histogram with the data frame.Plot the data frame in different order.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'a': [1, 1, 1, 1, 3],    'b': [1, 1, 2, 1, 3],    'c': [2, 2, 2, 1, 3], }) df.hist() df[['c']].hist() df[['a']].hist() df[['b']].hist() plt.show()Output

To put a legend outside the plot with Pandas, we can take the following Steps −Make a dictionary d with keys Column1 and Column2.Make a data frame using DataFrame (d).Plot the data frame with a list of styles.Using legend(), place a legend on the figure. The bbox_to_anchor keyword gives a great degree of control for manual legend placement. For example, if you want your axes legend located at the figure's top right-hand corner instead of the axes' corner, simply specify the corner's location, and the coordinate system of that location.To display the figure, use the show() method.Exampleimport pandas as pd from ... Read More

Using Pandas, we can create a dataframe and can create a figure and axes variable using subplot() method. After that, we can use the ax.scatter() method to get the required plot.StepsMake a list of the number of students.Make a list of marks that have been obtained by the students.To represent the color of each scattered point, we can have a list of colors.Using Pandas, we can have a list representing the axes of the data frame.Create fig and ax variables using subplots method, where default nrows and ncols are 1.Set the “Students count” label using plt.xlabel() method.Set the “Obtained marks” ... Read More

Using Pandas, we will create a dataframe and set the vertical lines on the created axes, using axvline lines.StepsUsing panda we can create a data frame.Creating a data frame would help to create help.Using axvline(), add a vertical line across the axes, where color is green, linestyle="dashed".Using axvline(), add a vertical line across the axes, where color is red, linestyle="dashed".Using plt.show(), show the plot.Exampleimport pandas as pd from matplotlib import pyplot as plt df = pd.DataFrame(index=pd.date_range("2019-07-01", "2019-07-31")) df["y"] = 1 ax = df.plot() ax.axvline("2019-07-24", color="green", linestyle="dashed") ax.axvline("2019-07-31", color="red", linestyle="dashed") plt.show()OutputRead More

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

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]