Pandas is another important Python module that is very popular among data scientists and machine learning developers. It provides many useful methods which can help to analyze the dataset in simple steps. The mean and medium of the dataframe in pandas can be found very easily with just one line of code by calling the method. In this article, we are going to learn how to plot mean of dataframe in Pandas using various methods. We will use Pandas, Matplotlib, and Searborn modules to plot the mean of the data frame.
How to plot the mean of DataFrame?
Mean is the average value of the data points. It is calculated by adding all the values and dividing by the number of values. We can plot the mean of the DataFrame using various modules, so first it is important to install these modules on your system.
Use the pip command which is a very popular and simple way of installing any Python module.
Importing and analyzing the dataset
Before going to the plotting of the mean, first, we need to import the dataset. In this section, we will use a sample dataset to learn the plotting of the mean.
Let us import the dataset and then analyze it.
# importing the module import pandas as pd # dataset data = pd.read_csv('Dushanbe_house.csv') data.head()
Unnamed: 0 number_of_rooms floor area latitude longitude price 0 0 1 1 58.0 38.585834 68.793715 330000 1 1 1 14 68.0 38.522254 68.749918 340000 2 2 3 8 50.0 NaN NaN 700000 3 3 3 14 84.0 38.520835 68.747908 700000 4 4 3 3 83.0 38.564374 68.739419 415000
We will now drop the first column and the NaN values from the dataset so that we will have clean data.
# dropping column data.drop('Unnamed: 0', axis=1, inplace=True) data.dropna(inplace=True)
Now the dataset is clean and clear. We can move toward the plotting of the mean.
Plot Mean of the Column of Dataframe
Let us assume that we want to plot the mean of the area column. First, we need to find the mean of the area column and then plot the graph with the original dataset:
# importing the module import matplotlib.pyplot as plt # finding the mean of the data df= data['area'] df.mean() # plotting the dataset plt.plot([i for i in range(len(df))], df) plt.plot([i for i in range(len(df))], [df.mean() for i in range(len(df))]) # plot show plt.show()
As you can see, the mean of the dataset is plotted along with the actual values using the Matplotlib module.
Finding Mean and Plotting Using Pandas
We will now find the means of all the columns and then plot them using a bar plot in pandas. Before plotting the mean values, we will drop the prices column because its value is very large and it will make our plotting ugly.
# plotting the mean values data.drop('price', axis=1).mean().plot(kind='bar')
As you can see, with just one line of code, we were able to show the mean of each of the columns in a bar chart using the Pandas module.
Using Describe() Method to Get Statistical Value
One of the important methods in Pandas is the describe() method which shows the statistical values of the dataframe. Let us use this method to get more inside our dataframe.
# describe method data.describe()
number_of_rooms floor area latitude longitude price count 3730.000000 3730.000000 3730.000000 3730.000000 3730.000000 3.730000e+03 mean 2.319035 6.553351 72.179893 38.553452 68.768399 5.348747e+05 std 1.036780 4.355972 33.330143 0.030199 0.056909 4.163996e+05 min 1.000000 0.000000 16.000000 37.511664 68.667721 4.500000e+02 25% 2.000000 3.000000 50.000000 38.530576 68.739065 3.200000e+05 50% 2.000000 5.000000 65.000000 38.560678 68.761022 4.500000e+05 75% 3.000000 10.000000 84.000000 38.572482 68.789177 6.300000e+05 max 6.000000 20.000000 370.000000 38.615876 71.509309 8.814000e+06
As you can see, the describe() method returns many important statistical values of the data frame.
Mean is the average value of the data points. It is calculated by taking the sum of all values and then dividing them by the total count of the values. In Pandas and Python, we have built-in methods to calculate the mean. In this short article, we learned how to plot the mean of the data frame using various methods in Python by taking examples.
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