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Pandas Articles
Page 42 of 42
Does pandas depend on NumPy?
Pandas is built on top of NumPy, which means the Python pandas package depends on the NumPy package and also pandas intended with many other 3rd party libraries. So we can say that Numpy is required for operating the Pandas.The pandas library depends heavily on the Numpy array for the implementation of pandas data objects.Exampleimport pandas as pd df = pd.DataFrame({'A':[1, 2, 3, 4], 'B':[5, 6, 7, 8]}) print('Type of DataFrame: ', type(df)) print('Type of single Column A: ', type(df['A'])) print('Type of values in column A', type(df['A'].values)) print(df['A'].values)Explanationdf variable stores a DataFrame object created by using python ...
Read MorePlot multiple columns of Pandas DataFrame using Seaborn
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
Read MoreAnnotate data points while plotting from Pandas DataFrame
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 MoreSetting Y-axis in Matplotlib using Pandas
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
Read MoreHow to change the order of plots in Pandas hist command?
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
Read MoreHow to give a Pandas/Matplotlib bar graph custom colors?
To make a custom color, we can create a hexadecimal string. From it, we can make different sets of color representation and can pass them into the scatter method to get the desired output.Using the set_color method, we could set the color of the bar.StepsTake user input for the number of bars.Add bar using plt.bar() method.Create colors from hexadecimal alphabets by choosing random characters.Set the color for every bar, using set_color() method.To show the figure we can use plt.show() method.Examplefrom matplotlib import pyplot as plt import random bar_count = int(input("Enter number of bars: ")) bars = plt.bar([i for ...
Read MoreHow to select subsets of data In SQL Query Style in Pandas?
IntroductionIn this post, I will show you how to perform Data Analysis with SQL style filtering with Pandas. Most of the corporate company’s data are stored in databases that require SQL to retrieve and manipulate it. For instance, there are companies like Oracle, IBM, Microsoft having their own databases with their own SQL implementations.Data scientists have to deal with SQL at some stage of their career as the data is not always stored in CSV files. I personally prefer to use Oracle, as the majority of my company’s data is stored in Oracle.Scenario – 1 Suppose we are given a ...
Read MoreHow to Handle Large CSV files with Pandas?
In this post, we will go through the options handling large CSV files with Pandas.CSV files are common containers of data, If you have a large CSV file that you want to process with pandas effectively, you have a few options.Pandas is an in−memory toolYou need to be able to fit your data in memory to use pandas with it. If you can process portions of it at a time, you can read it into chunks and process each chunk. Alternatively, if you know that you should have enough memory to load the file, there are a few hints to ...
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