Data Analysis · Data Mining · Pandas · Python · SciKit-Learn

Impute NaN values with mean of column Pandas Python

Incomplete data or a missing value is a common issue in data analysis. Systems or humans often collect data with missing values. Actually, we can do data analysis on data with missing values, it means we do not aware of the quality of data. However, it may produce the wrong results because of those missing values. The common approach to deal with missing value is dropping all tuples that have missing values. The problem with this dropping approach is it may generate bias results especially if the rows that contain NaN values are large, while in the end, we have to drop a large number of tuples. This way can be used if the data has a small number of missing values. In the case of data with a large number of missing values, we have to repair those missing values.

There are a lot of proposed imputation methods for repairing missing values. The simplest one is to repair missing values with the mean, median, or mode. It can be the mean of whole data or mean of each column in the data frame.

In this experiment, we will use Boston housing dataset. The Boston data frame has 506 rows and 14 columns. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The Boston house-price data has been used in many machine learning papers that address regression problems. MEDV attribute is the target (dependent variable), where others are independent variables. This dataset is available in the scikit-learn library, so we can just import it directly.  As usual, in this experiment, I am going to use Python Jupyter notebook. If you are not familiar with Jupyter Notebook, Pandas, Numpy, and other python libraries, I have a couple of old posts that may useful for you: 1) setup anaconda 2) understand python libraries for data science.

Let’s get started…


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston

boston_bunch = load_boston()
dfx = pd.DataFrame(boston_bunch.data, columns = boston_bunch.feature_names) #independent variables
dfy = pd.DataFrame(boston_bunch.target, columns = ['target']) #dependent variables
boston = dfx.join(dfy)
)

We can use command boston.head() to see the data, and boston.shape to see the dimension of the data. The next step is check the number of Na in boston dataset using command below.


boston.isnull().sum()

The result shows that Boston dataset has no Na values. The question is how we create/change some values to NA? is that possible?


#Change some values (20%) to NAN randomly
# Change 10% Values to NA randomly
import collections
import random
df = boston
replaced = collections.defaultdict(set)
ix = [(row, col) for row in range(df.shape[0]) for col in range(df.shape[1])]
random.shuffle(ix)
to_replace = int(round(.2*len(ix)))
for row, col in ix:
    if len(replaced[row]) < df.shape[1] - 1:
        df.iloc[row, col] = np.nan
        to_replace -= 1
        replaced[row].add(col)
        if to_replace == 0:
            break

Using the code above, we can replace some values (20%) in Boston dataset to NA. We also can change the percentage of NA by changing the code above(see .2*). Na values are absolutely random with respect to the whole data. Then, now check again is there any missing values in our boston dataset?


boston.isnull().sum()

The result shows that all columns have around 20% NaN values. Then how to replace all those missing values (impute those missing values) based on the mean of each column?


#fill NA with mean() of each column in boston dataset
df = df.apply(lambda x: x.fillna(x.mean()),axis=0)

Now, use command boston.head() to see the data. We have fixed missing values based on the mean of each column. We also can impute our missing values using median() or mode() by replacing the function mean().

This imputation method is the simplest one, there are a lot of sophisticated algorithms (e.g., regression, monte carlo, etc) out there that can be used for repairing missing values. Maybe I’ll post it next time.

All codes and results can be accessed through this link https://github.com/rischanlab/PyDataScience.Org/blob/master/python_notebook/2%20Impute%20NaN%20values%20with%20mean%20of%20each%20columns%20.ipynb

Thank you, see you

Data Analysis · Data Mining · NumPy · Pandas · SciKit-Learn

Remove Duplicates from Correlation Matrix Python

Correlation is one of the most important things that usually used by the data analysts in their analytical workflow. By using correlation, we can understand the mutual relationship or association between two attributes. Let’s start with the example. For instance, I want to do an analysis of the “Boston housing dataset”, let see the example code below. If you are not familiar with Jupyter Notebook, Pandas, Numpy, and other python libraries, I have a couple of old posts that may useful for you: 1) setup anaconda 2) understand python libraries for data science.


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets

boston = datasets.load_boston() #Load Boston Housing dataset, this dataset is available on Scikit-learn
boston = pd.DataFrame(boston['data'], columns=boston['feature_names'])
)

We can use the command boston.head() to see the data, and boston.shape to see the dimension of the data. We can easily use this command below to get correlation value among all attributes in Boston housing dataset. (e.g., in this experiment I used Pearson correlation).


dataCorr = boston.corr(method='pearson')
dataCorr

After using this command, we will see the matrix of correlation like in Figure below:

The question is, how to remove duplicates from this matrix of correlation to make it more readable? I found a nice answer on stackoverflow, we can use this command:


dataCorr = dataCorr[abs(dataCorr) >= 0.01].stack().reset_index()
dataCorr = dataCorr[dataCorr['level_0'].astype(str)!=dataCorr['level_1'].astype(str)]

# filtering out lower/upper triangular duplicates 
dataCorr['ordered-cols'] = dataCorr.apply(lambda x: '-'.join(sorted([x['level_0'],x['level_1']])),axis=1)
dataCorr = dataCorr.drop_duplicates(['ordered-cols'])
dataCorr.drop(['ordered-cols'], axis=1, inplace=True)

dataCorr.sort_values(by=[0], ascending=False).head(10) #Get 10 highest correlation of pairwaise attributes

Finally, we get the table that consists of the pair of attributes and the correlation values, and the most important thing is we do not have any duplication.

I also found another way from a guy in Github who create a nice function to remove this duplication. Please see through this link.

Thank you and see you next time.

Data Analysis · NumPy · Pandas

Python Pandas DataFrame Basics Tutorial

In this post, I am going to show you how to deal with data in Python. Before going there, you have to understand what kind of python libraries that you need to know if you want to deal with data in Python. Python has tons of libraries especially related to data science. I have a couple of old posts that may useful for you: 1) setup anaconda 2) understand python libraries for data science. In this tutorial, I use Jupyter Notebook, if you did not have/familiar yet, please read the instruction above, otherwise, just go down!

Let start from the simplest one. When you want to deal with data in Python. Python has an amazing library called Pandas. If you are familiar with Spreadsheet tool such as MS Excel, Pandas similar to that kind of tool, Pandas shows our data in the format of Table. The only difference is, when you use Excel you just drag and drop but here in Pandas, you have to understand the standard syntax and command of pandas.

Let’s get started.

To start analyzing data, you can import your data (e.g., csv, xls, and etc) to python environment using Pandas: import pandas as pd then pd.read_csv('data.csv'). However, to make it easier, in this tutorial, I just create my data rather than import from file.


#Create DataFrame
import pandas as pd #when we want to use Pandas, we have to import it
import numpy as np #numpy is another useful library in python for dealing with number
df = pd.DataFrame(
    {'integer':[1,2,3,6,7,23,8,3],
     'float':[2,3.4,5,6,2,4.7,4,8],
     'string':['saya','aku', np.NaN ,'cinta','kamu','a','b','indonesia']}
)

1. To show your DataFrame, just use this command!

#show data in DataFrame
df

2. If you want to access your single or more data from your DataFrame, you can access it using loc syntax.

#Show data based on index
df.loc[1]

3. If you only need some columns and ignore other columns, you can just select the columns:

#show data based on columns selected
df[['string','float']]

4. You also can apply IF condition on your data similar to the filter in Excel. Use this command and see your result.

#show data with condition
df[df['float']>4]

5. You also able to rename the columns by using this command:

#rename column in DataFrame
df.rename(columns={'string':'char'})

6. When I create the data, I add one row that contains Nan or null value. The missing value is a common issue in data. So, how to deal with missing values? first, we need to know whether our DataFrame contains missing values or not by using this command.

#Show NaN value in DataFrame
df.isnull()

7. The simplest way to deal with missing values is to drop all missing values. How to drop missing values? here the command:

# Drop all rows contain NaN value
df.dropna()

8. We also can make summaries from our data (e.g., mean, median, mode, max, etc), use this command and see what you got!

#Show mean, median, and maximum in Data Frame
mean = df['float'].mean()
print("mean =", mean)
median = df['float'].median()
print("median = ", median)
max = df['float'].max()
print("max =", max)

Here the result: https://github.com/rischanlab/PyDataScience.Org/blob/master/python_notebook/1%20Basic%20Pandas%20Operation%20.ipynb

 

 

Machine Learning · Matplotlib · NumPy · Pandas · SciKit-Learn

Linear Regression using Python

Whoever wants to learn machine learning or become a data scientist, the most obvious thing to learn first time is linear regression. Linear regression is the simplest machine learning algorithm and it is generally used for forecasting. The goal of linear regression is to find a relationship between one or more independent variables and a dependent variable by fitting the best line. This best fit line is known as regression line and defined by a linear equation Y= a *X + b.

For instance, in the case of the height of children vs their age. After collecting the data of children height and their age in months, we can plot the data in a scatter plot such as in Figure below.

 

Linear regression will find the relationship between age as the independent variable and height as the dependent variable. Linear regression will find the best fit line from all points on the scatter plot. Finally, it can be used as a prediction, for instance, to predict what height the children when his age enter 35 months?

 

How to implement this linear regression in Python?

First, to make easier, I will generate a random dataset for our experiment.


import pandas as pd
import numpy as np

np.random.seed(0)
x = np.random.rand(100, 1) #generate random number for x variable
y = 2 + 3 * x + np.random.rand(100, 1) # generate random number of y variable
x[:10], y[:10] #Show the first 10 rows of each x and y

 

There are many ways to build a regression model, we can build it from scratch or just use the library from Python. In this example, I use scikit-learn


from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from matplotlib import pyplot as plt

# Initialize the model
model = LinearRegression()
# Train the model - fit the data to the model
model.fit(x, y)
# Predict
y_predicted = model.predict(x)

# model evaluation
rmse = mean_squared_error(y, y_predicted)
r2 = r2_score(y, y_predicted)

# printing values
print('Slope:' ,model.coef_)
print('Intercept:', model.intercept_)
print('Root mean squared error: ', rmse)
print('R2 score: ', r2)

# plotting values
plt.scatter(x, y, s=5)
plt.xlabel('x')
plt.ylabel('y')

# predicted values
plt.plot(x, y_predicted, color='r')
plt.show()

Tarraaa!, it’s easy right?

See you next time

Matplotlib · NumPy · Pandas · Plotting in Python

Read the data and plotting with multiple markers

Let’s assume that we have an excel data and we want to plot it on a line chart with different markers. Why markers? just imagine, we have plotted a line chart with multiple lines using a different colour, but we only have black and white ink, after printing, all lines will be in black colour. That’s why we need markers.

 

For instance, our data can be seen in Table above, this is just a dummy data that tells about algorithms performance vs. the number of k. We want to plot this data to the line chart.  We already have the previous experiment, how to plot the line chart with multiple lines and multiple styles. However, in the previous experiment, we used static declaration for each line. It will be hard if we have to declare one by one for each line.

Let’s get started

The first step is to load our Excel data to the DataFrame in pandas.


import pandas as pd
import numpy as np

import matplotlib as mpl

xl = pd.ExcelFile("Experiment_results.xlsx")

df = xl.parse("Sheet2", header=1, index_col=0)
df.head()

It’s very easy to load Excel data to DataFrame, we can use some parameters which very useful such as sheet name, header, an index column. In this experiment, I use “Sheet2″ due to my data in the Sheet2, and I use ”1″ as the header parameter which means I want to load the header to the DataFrame, and if you don’t want to load it just fill it with ”0″. I also use index_col equal to “0”, which means I want to use the first column in my Excel dataset as the index in my DataFrame. Now we have a dataframe that can be seen in the Table above.

The second step is how to set the markers. As I said in the previous experiment that matplotlib supports a lot of markers. Of course, I don’t want to define one by one manually. Let see the code below:


# create valid markers from mpl.markers
valid_markers = ([item[0] for item in mpl.markers.MarkerStyle.markers.items() if
item[1] is not 'nothing' and not item[1].startswith('tick') and not item[1].startswith('caret')])

# valid_markers = mpl.markers.MarkerStyle.filled_markers

markers = np.random.choice(valid_markers, df.shape[1], replace=False)

Now, we have a list of markers inside the ‘markers’ variable. We need to select the markers randomly which are defined by df.shape[1] (the number of columns). Let start to plot the data.


ax = df.plot(kind='line')
for i, line in enumerate(ax.get_lines()):
line.set_marker(markers[i])

# adding legend
ax.legend(ax.get_lines(), df.columns, loc='best')
plt.show()

Taraaaa!!!!, it’s easy, right?

 

 

The next question is how to plot Figure like below?

plotting-multiplelines-with multiple styles

Check it out here. 

Data Analysis · Matplotlib · Plotting in Python

Plot multiple lines in one chart with different style Python matplotlib

Sometimes we need to plot multiple lines in one chart using different styles such as dot, line, dash, or maybe with different colour as well. It is quite easy to do that in basic python plotting using matplotlib library.

We start with the simple one, only one line:


import matplotlib.pyplot as plt
plt.plot([1,2,3,4])

# when you want to give a label
plt.xlabel('This is X label')
plt.ylabel('This is Y label')
plt.show()

 

Let’s go to the next step, several lines with different colour and different styles.


import numpy as np
import matplotlib.pyplot as plt

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()

If only three lines, it seems still easy, how if there are many lines, e.g: six lines.


import matplotlib.pyplot as plt
import numpy as np

x=np.arange(6)

fig=plt.figure()
ax=fig.add_subplot(111)

ax.plot(x,x,c='b',marker="^",ls='--',label='Greedy',fillstyle='none')
ax.plot(x,x+1,c='g',marker=(8,2,0),ls='--',label='Greedy Heuristic')
ax.plot(x,(x+1)**2,c='k',ls='-',label='Random')
ax.plot(x,(x-1)**2,c='r',marker="v",ls='-',label='GMC')
ax.plot(x,x**2-1,c='m',marker="o",ls='--',label='KSTW',fillstyle='none')
ax.plot(x,x-1,c='k',marker="+",ls=':',label='DGYC')

plt.legend(loc=2)
plt.show()

Now, we can plot multiple lines with multiple styles in one chart.

These are some resources from matplotlib documentation that may useful:

  1. Marker types of matplotlib https://matplotlib.org/examples/lines_bars_and_markers/marker_reference.html
  2. Line styles matplotlib https://matplotlib.org/1.3.1/examples/pylab_examples/line_styles.html
  3. Matplotlib marker explanation https://matplotlib.org/api/markers_api.html

In this experiment, we define each line manually while it can be hard if we want to generate line chart from dataset. In the next experiment, we use real an Excel dataset and plot the data to line chart with different markers without defining one by one for each line -> just check it here https://pydatascience.org/2017/12/05/read-the-data-and-plotting-with-multiple-markers/

*Some part of the codes, I took from StackOverflow

Anaconda · Data Analysis

Python for Data Science using Anaconda

A few years ago, I had a complate setup environment for Python data analysis on my Macbook. I had Python with python virtual environments (virtualenv), of course, one of my virtualenv has complete data science libraries. I used this virtualenv when I want to do data analysis. I also had experiences using docker-machine to be more productive and reproducible in analyzing data but it is too heavy on my Laptop so I kept using virtualenv. I have heard about Anaconda or conda which is the platform that bundles all data science libraries to one plate, but I never tried yet. It is hard for me to move on.

After I start my PhD study in UQ Australia, I have a new computer in my office and it is Windows. I wanted to start working with data using Python in that computer and I tried to remember all steps that I have to do,  so I can do data analysis on my office computer: Installing Python, installing PIP, installing virtualenv, installing virtualenv wrapper, installing all data science libraries to one of my virtualenv, and start working!

As a lazy guy, I do not want to do that. I went to anaconda website and I decided to try Anaconda and tarrraa!!!!!!!!

Just go to https://www.anaconda.com/download/ download the installer which matches with your operating system, install it then launch the Anaconda Navigator.

 

It was surprising me, I even can run Rstudio using Anaconda Navigator. If you enjoy using Jupyter (IPython Notebook), just press launch for the Jupyter. It is also has one of beautiful IDE to do data analysis in Python which is Spider. It is really cool! Previously when I used R, I always use Rstudio as my IDE to do data analysis and now if you want to move to Python, you can use a similar IDE which is Spider.

If you want to know what kind of data science libraries that you need to install manually if you don’t want to use Anaconda, please visit this link. The picture below describes some Python libraries for Data Science which may useful for you: