Predict Product Attributes from Product Listings Part 2 – Pipelines & GridSearch

Further improvement on the Product Attributes Text Classifier

This is part 2 of the extracting attributes from product title with the following improvements or add on.

  1. Creating a more generic text cleaning function.
  2. Adding GridSearch for hyper parameters tuning.

Text Cleaning Function

I created a more generic text cleaning function that can accommodate various text data sets. This can use as a base function for text related problem set. The function, if enabled all options, will be able to perform the following:

  1. Converting all text to lowercase.
  2. Stripping html tags especially if data is scrapped from web.
  3. Replacing accented characters with closest English alphabets/characters.
  4. Removing special characters which includes punctuation. Digits may or may not be excluded depending on context. (Digits are not removed for this data set)
  5. Removing stop-words (simple vs detailed. If detailed, will tokenize words before removal else will use simple word replacement.
  6. Removing extra white spaces and newlines.
  7. Normalize text. This either refer to stemming or lemmatizing.

In this example, we only turn on:

  1. converting text to lowercase
  2. remove special characters (need to keep digits) and white spaces,
  3. do a simple stop words removal.

As mentioned in previous post, it is likely a seller would not include much stop words and will try to keep the title as concise as possible given the limited characters and also to make the title more relevant to search engine. As the text length is not too long, will skip normalizing text to save time.

# Text pre-processing modules
from bs4 import BeautifulSoup
import unidecode
import spacy, en_core_web_sm
nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
STOPWORDS = set(stopwords.words('english')) 

# Compile regular expression
SPEC_CHARS_REPLACE_BY_SPACE = re.compile('[/(){}\[\]\|@,;]')
SPEC_CHARS = re.compile(r'[^a-zA-z0-9\s]')
SPEC_CHARS_INCLUDE_DIGITS = re.compile(r'[^a-zA-z\s]')
EXTRA_NEWLINES = re.compile(r'[\r|\n|\r\n]+')

## Functions for text preprocessing, cleaning

def strip_htmltags(text):
    soup = BeautifulSoup(text,"lxml")
    return soup.get_text()

def replace_accented_chars(text):
    return unidecode.unidecode(text)

def stem_text(text):
    ps = PorterStemmer()
    modified_txt = ' '.join([ps.stem(word) for word in text.split()])
    return modified_txt    

def lemmatize(text):
    modified_text = nlp(text)
    return ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in modified_text])

def normalize(text, method='stem'):
    """ Text normalization to generate the root form of the inflected words.
        This is done by either "stem" or "lemmatize" the text as defined by the 'method' arguments.
        Note that using "lemmatize" will take much longer to run compared to "stem".
    if method == 'stem':
        return stem_text(text)
    if method == 'lemmatize':
        return lemmatize(text)
    print('Please choose either "stem" or "lemmatize" method to normalize.')
    return text

def rm_special_chars(text, rm_digits=False):
    # remove & replace below special chars with space
    modified_txt = SPEC_CHARS_REPLACE_BY_SPACE.sub(' ', text)

    # remove rest of special chars, no replacing with space
    if rm_digits:
        return SPEC_CHARS_INCLUDE_DIGITS.sub('', modified_txt)
        return SPEC_CHARS.sub('', modified_txt)

def rm_extra_newlines_and_whitespace(text):
    # rm extra newlines
    modified_txt =  EXTRA_NEWLINES.sub(' ', text)

    # rm extra whitespaces
    return re.sub(r'\s+', ' ', modified_txt)

def rm_stopwords(text, simple=True):
    """ Remove stopwords using either the simple model with replacement.
        or using nltk.tokenize to split the words and replace each words. This will incur speed penalty.
    if simple:
        return ' '.join(word for word in text.split() if word not in STOPWORDS)
        tokens = word_tokenize(text)
        tokens = [token.strip() for token in tokens]
        return ' '.join(word for word in tokens if word not in STOPWORDS)

def clean_text(raw_text, strip_html = True, replace_accented = True,
                normalize_text = True, normalize_methd = 'stem',
                remove_special_chars = True, remove_digits = True,
                remove_stopwords = True, rm_stopwords_simple_mode = True):

    """ The combined function for all the various preprocessing method.
        Keyword args:
            strip_html               : Remove html tags.
            replace_accented         : Convert accented characters to closest English characters.
            normalize_text           : Normalize text based on normalize_methd.
            normalize_methd          : "stem" or "lemmatize". Default "stem".
            remove_special_chars     : Remove special chars.
            remove_digits            : Remove digits/numeric as special characters.
            remove_stopwords         : Stopwords removal basedon NLTK corpus.
            rm_stopwords_simple_mode : skip tokenize before stopword removal. Speed up time.

    text = raw_text.lower()

    if strip_html:
        text = strip_htmltags(text)
    if replace_accented:
        text = replace_accented_chars(text)
    if remove_special_chars:
        text = rm_special_chars(text, remove_digits)
    if normalize_text:
        text = normalize(text, normalize_methd)
    if remove_stopwords:
        text = rm_stopwords(text, rm_stopwords_simple_mode)

    text = rm_extra_newlines_and_whitespace(text)  

    return text

Grid Search for Hyper Parameters Tuning

Using pipelines, it is easy to incorporate the sklearn grid search to sweep through the various the hyper parameters and select the best value. Two main parameters tuning are:

  1. ngram range in CountVectorizer:
    • In the first part, we only looking a unigram or single word but there are some attributes that are identified by more than one word alone (eg 4G network, 32GB Memory etc) therefore we will sweep the ngram range to find the optimal range.
    • The larger the ngram range the more feature columns will be generated so it will be more memory consuming.
  2. alpha in SGDClassifier
    • This will affect the regularization term and the learning rate of the training model.

With the ngram range and alpha parameters sweep and the best value selected, we can see quite a significant improvement to the accuracy to all the attribute prediction compared to the first version. Most of the improvement comes from the ngram adjusted to (1,3), meaning account for trigram. This is within expectation as more attributes are described by more than one word.

# Prepare model -- Drop na and keep those with values
def get_X_Y_data(x_col, y_col):
    sub_df =  df[[x_col, y_col]]
    sub_df = sub_df.dropna()
    return sub_df[x_col], sub_df[y_col]

# Model training & GridSearch
def generate_model(X, y, verbose = 1):

    text_vect_pipe = Pipeline([
                            ('vect', CountVectorizer()),
                            ('tfidf', TfidfTransformer())

    pred_model = Pipeline([
                ('process', text_vect_pipe),
                ('clf', SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42, max_iter=5, tol=None))

    parameters = {}
    parameters['process__vect__ngram_range'] = [(0,1),(1,2),(1,3)]
    parameters['clf__loss'] = ["hinge"]
    parameters['clf__alpha'] = [5e-6,1e-5]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)

    CV = GridSearchCV(pred_model, parameters), y_train)
    y_pred = CV.predict(X_test)

    print('accuracy %s' % accuracy_score(y_pred, y_test))
    print("Details of GridSearch")

    if verbose:
        print('Best score and parameter combination = ')
        print("Grid scores on development set:")
        means = CV.cv_results_['mean_test_score']
        stds = CV.cv_results_['std_test_score']
        for mean, std, params in zip(means, stds, CV.cv_results_['params']):
            print("%0.3f (+/-%0.03f) for %r"
                  % (mean, std * 2, params))

    return CV

X, y = get_X_Y_data('title1', 'Brand')
brand_model = generate_model(X, y)

The full script is as below. The text cleaning function takes a large part of the code. Excluding the function, the additional of few lines of code for the grid search and pipeline can can bring a relatively significant accuracy improvement.

Next Actions

So far only text features are considered, the next part we will try adding numeric features to see if further improvement can be made.

See Also

  1. Predict Product Attributes from Product Listing Title — Text Feature Extraction and Classification



Easy Create Mosaic Plot using Stacked Bar Chart

Creating Mosaic Plot

In one of my work project, I need to use mosaic plot to visualize the proportion of different variables/elements exists in each group.  It is hard to find a readily available mosaic plot function (from Seaborn etc) which can be easily customized. By reading some of the blogs, mosaic plot can be created using stacked bar chart concept by performing some transformation on the raw data and overlaying individual bar charts. With this knowledge and using python Pandas and Matplotlib, I am able to create a mosaic plot that is good enough for my need.

Sample Data Sets

A sample data set is as shown below. We need to plot the proportion of b, g, r (all the columns) for each index (0 to 4). Based on the format of the data set, we make a transformation of the columns to be able to have Mosaic Plot.

Breaking down the data transformation for stacked bar chart plotting

We perform two transformations as followed. Mosaic plot requires the sum of  proportion of categories for each group to be 1.0 or 100%. Stacked bar chart can achieve this by summing or stacking values for each element in the group but we would need to ensure the values are normalized and the sum of all elements in a group equal to 1 (i.e r+ g+b =1 for each index).

To simulate the effect of stacked bar chart , the trick is to use multiple bar charts to overlay on top of each other to simulate the effect of stacked bar chart. To be able to create the stacked effect, the ratio/proportion of the stacked element need to be the sum of proportion value of “bottom” elements + the proportion value of the element itself. This can be easily achieved by doing a cumulative sum along the row axis.

As example below, r will be used as a base (since values are based on b + g + r). g will overlay on top of r since it is summation of b + g. b will be final layer overlay on g and r.

Mosaic plot function

Once the transformations are done, it is easy to plot the mosaic plot by plotting the different bar charts and overlaying on top of each other. Additional module adjustText can be used to prevent overlapping of the text labels in the plot. Based on the above, we can create a general mosaic function as below.


Predict Product Attributes From Product Listing Title — Text Feature Extraction and Classification

Extracting Attributes from Product Title and Image

This is a National (Singapore) Data Science Challenge organised by Shopee hosted on Kaggle. In the advanced category, the tasks is to extract a list of attributes from each product listing given product title and the accompanied image (a text and a image input). Training sets and full instructions are available in the Kaggle link. This is a short attempt of the problem which include the basic data exploration, data cleaning, feature extraction and classification.

Basic Data Exploration

While the project requirement have 3 main product categories, Beauty, Mobile, & Fashion, I will just focus on the Mobile data set. The two other categories will follow the same approach. For the mobile data set, the requirement is to extract the following attributes such as Brand, Phone Model, Camera, Phone Screen Size, Color Family.  A brief exploration of the training data set observed.

  1. Only title (text) & image (pic) available to predict the several attributes
    of the product.
  2. The attributes are already label-encoded.
  3. There are a lot of missing values particularly like Network Connections etc have more than 80% of data missing. This is quite expected as sellers unlikely to put some of these more obscured attributes in the title description while attributes like Brand and Model should have less missing data.

From seller’s perspective, seller will try to include as much information as possible in a
concise manner especially attributes like brands, models etc to make their posting relevant to search and stand out to the buyers. Using only image to extract attributes such as Brand and model might be difficult especially for mobile category where it is difficult to differentiate from pic even with human eye.

From the exploration, I planned the following steps.

  1. Using title (text) as main classification input and ignore images.
  2. Train and predict each attribute at a time.

Basic Data and Text Cleaning

There are some attributes Network Connections, Warranty Period which have large proportion of missing data. However, those attributes have majority of the observations having a certain attribute. In this case, those missing values are assigned with the mode of the training population (e.g. it is likely for Network Connections , most phones should be 4G etc). The attributes are also converted to integer for training purpose.

For the title, before extracting the numeric features, we perform cleaning on the data set. Since most users would highlight the most important feature in the product tile to make their product stand out and relevant, they would generally have omitted most of the stop words, most punctuation. and white spaces Hence for this data set, I will try minimal cleaning: change the title to lowercase and remove special characters. This can reduce a significant amount of time in text cleaning especially for large data set.

Data Cleaning and pipelines

For the advanced data extraction, I chose the Bag-Of-Word (BOW) model to generate the features from the text columns. In the BOW model, I use TF-IDF approach which computes the weighted frequency of each word in each title. For classification, SVM is chosen as the classifier. Pipe-lining makes it easy to streamline the whole text processing and attributes classification making it run on all the different attributes.

Below is the complete code running from extraction, cleaning to classification.

Further Improvement

This is the starting point of the project and take only a few lines of code to get it up and running for quick analysis.  I will improve the existing code by incorporate gridsearch for hyperparameters and expanding on the pipelines and features in the subsequent posts.

See Also

  1. Predict Product Attributes from Product Listings Part 2 – Pipelines & GridSearch


Using k-means clustering to detect abnormal profile or sudden trough


For a particular test we are handling, we need to ensure a particular metric A maintain a certain parabolic or relatively flat profile across a range of metric B. In recent days, we encountered an issue where certain samples of the population are experiencing a significant and sudden drop in metric A within a sub range of metric B.

We need to comb through the population to detect those that has the abnormal profile as shown in chart below for further failure analysis. While it is easy to identify by eye which sample are seeing abnormal performance after plotting metric B against metric A, it is impossible to scan through all the plots to identify the problem sample.


I decide to use machine learning to comb through the population to get the defective samples. Given the limited training samples on hand and the hassle of getting more data, I will use unsupervised learning for quick detection in this case.

** Note the examples below are set to be to randomly generated as model to the real data set.


There are certain pre-processing done on actual data but not on the sample data. Some of the usual pre-processing tasks performed are illustrated below.

  1. check and remove missing data (can use pd.isnan().sum()
  2. drop non required columns (pd.drop())

Features Engineering

To detect the abnormal profile, I need to build the features that might be able to differentiate normal vs abnormal profile. Below are some of the features I can think of which is derived by aggregating Metric A measured across all Metric B for each sample:

  1. Standard deviation of Metric A
    • Abnormal profile will have larger stddev due to the sharp drop.
  2. Range of Metric A
    • larger range of max – min for the abnormal profile.
  3. Standard deviation of Running delta of Metric A
    • Running delta is defined as the delta of Metric A for particular Metric B against Metric A of previous Metric B. A sudden dip in Metric A will be reflected in the sudden large delta.
    • Standard deviation of the running delta will catch the variation in the rise and dip.
  4. Max of Running delta of Metric A
    • This will display the largest delta within a particular sample.

Scaling and K-means Clustering

A basic scaling is done to normalize the features before applying the KMeans. All the functions will be from SkLearn. KMeans cluster is set to 2 (normal vs abnormal profile)


This is a short and quick way to get some of the samples out for failure analysis but will still need further fine tuning if turn on for production modes.

Sample Script


Convert Jupyter Notebook into Gist fast with Gist-it

Easy way to convert Jupyter Notebook into Gist.

  1. Required Tools:
      1. Jupyter extension package
  2. Steps:
      1. Install Jupyter extension and configurator
      2. Commands
        1. pip install jupyter_contrib_nbextensions
        2. jupyter contrib nbextension install
        3. pip install jupyter_nbextensions_configurator
        4. jupyter nbextensions_configurator enable
  3. Open notebook and there will be a new tab Nbextensions
  4. Select Gist it and enable it. See step 5 for further configuration.
  5. Note: somehow I cannot create anonymous gist even though Gist-it allows it. Therefore, would need to create a access token from Github.
  6. To generate the access token, go to link and click “generate  new token”. Provide a description and under scope, tick gist and click Generate token
  7. Copy the token string. Return to Gist-it parameters selection in Notebook and Copy the token into the GitHub personal access token. Tick Gists default to public and click Enable
  8. To gist a notebook, click on the Github icon, tick Make the gist public and enter a description, click Gist it!

Further notes 

Retrieving Stock statistics from Yahoo Finance using python

For this post, we are only going to scrape the “Key Statistics” page of a particular stock in Yahoo Finance. The usual way might be to use Requests and BeautifulSoup to parse the web page. However, with the table format in the targeted webpage, it is easier to use Pandas read_html and DataFrame function.

  1. Objectives:
      1. Retrieving stocks information (Key statistics) from Yahoo Finance.
  2. Required Tools:
      1. Python Pandas—  Using Pandas read_html function for reading web table form.

Usage — Pulling a particular stock data data

import pandas as pd

tgt_website = r''

def get_key_stats(tgt_website):

    # The web page is make up of several html table. By calling read_html function.
    # all the tables are retrieved in dataframe format.
    # Next is to append all the table and transpose it to give a nice one row data.
    df_list = pd.read_html(tgt_website)
    result_df = df_list[0]

    for df in df_list[1:]:
        result_df = result_df.append(df)

    # The data is in column format.
    # Transpose the result to make all data in single row
    return result_df.set_index(0).T

# Save the result to csv
result_df = get_key_stats(tgt_website)

Pulling all the stocks symbols

Here, we are pulling one known stock symbol. To get all the stocks in particular indices, the stock symbols need to be known first. The below code will extract all the stock symbols, along with other data, from the NASDAQ website.

import pandas as pd

weblink = ''
sym_df = pd.read_csv(weblink)
stock_symbol_list = sym_df.Symbol.tolist()

Pulling key statistics for all stock symbols (for given index)

The last step will be to iterate all the symbols and get the corresponding key statistcis

all_result_df = pd.DataFrame()
url_prefix = '{0}/key-statistics?p={0}'
for sym in stock_symbol_list:
    stock_url = url_prefix.format(sym)
    result_df = get_key_stats(stock_url)
    if len(all_result_df) ==0:
        all_result_df = result_df
        all_result_df = all_result_df.append(result_df)

# Save all results
all_result_df.to_csv('results.csv', index =False)


Monitoring quality over time with heap map

A particular concern with testing hard disk drives over multiple times is the quality of certain drives may degrade (wear and tear) over time and we failed to detect this degradation.

We have certain metrics to gauge any degradation symptom observed for a particular head in a particular drive. For example, with metric A, we are looking at the % change over time reference to the date of the first test o determine whether a head is degraded.

Below python code will base on the following table to generate the required heatmap for easy visualization.


Calculating %Change

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

df1['DATE1'] = df1.DATE.dt.strftime('%m/%d/%Y')
df1 = df1.sort_values(by = 'DATE1')

# calculate the metric % change and
# actual change with reference to each individual head first data

df1['METRIC_A_PCT_CHANGE'] = df1.groupby(['SERIAL','HEAD'])['METRIC_A']\
                            .apply(lambda x: x.div(x.iloc[0]).subtract(1).mul(100))
df1['METRIC_A_CHANGE'] = df1.groupby(['SERIAL','HEAD'])['METRIC_A']\
                         .apply(lambda x: x - x.iloc[0])

Plotting in HeapMap

fig, ax = plt.subplots(figsize=(10,10))

# Pivot it for plotting in heap map
ww = df1.pivot_table(index = ['SERIAL','HEAD'], \
                     columns = 'DATE1', values = "METRIC_A_PCT_CHANGE")

g = sns.heatmap(ww, vmin= -5, vmax = 5, center = 0, \
                cmap= sns.diverging_palette(220, 20, sep=20, as_cmap=True),\
                xticklabels=True, yticklabels=True, \
                ax = ax, linecolor = 'white', linewidths = 0.1, annot = True)

g.set_title("% METRIC_A changes over multiple Dates", \
            fontsize = 16, color = 'blue')


Generated Plots

From the heap map, SER_3BZ-0 have some indication of degradation with increasing % Metric A loss over the different test date.



  • Getting the % percentage change relative to first value of each group.
    • df.groupby(‘security’)[‘price’].apply(lambda x: x.div(x.iloc[0]).subtract(1).mul(100))


Downloading YouTube Videos and converting to MP3

A simple guide to download videos from YouTube using python

  1. Objectives:
      1. Download YouTube Videos
      2. Saving as subclip (saving a portion of the video)
      3. Converting to MP3
  2. Required Tools:
      1. PyTube— primarily for downloading youtube videos.
      2. MoviePy — for video editing and also convert to mp3.
  3. Steps:
    1. pip install pytube and moviepy

Basic Usage

from pytube import YouTube
from moviepy.editor import *

# download a file from youtube
youtube_link = ''
w = YouTube(youtube_link).streams.first()"/your/target/directory")

# download a file with only audio, to save space
# if the final goal is to convert to mp3
youtube_link = ''
y = YouTube(youtube_link)
t = y.streams.filter(only_audio=True).all()

Downloading videos from a YouTube playlist

import requests
import re
from bs4 import BeautifulSoup

website = ''
r= requests.get(website)
soup = BeautifulSoup(r.text)

tgt_list = [a['href'] for a in soup.find_all('a', href=True)]
tgt_list = [n for n in tgt_list if'watch',n)]

unique_list= []
for n in tgt_list:
    if n not in unique_list:

# all the videos link in a playlist
unique_list = ['' + n for n in unique_list]

for link in unique_list:
    y = YouTube(link)
    t = y.streams.all()

Converting from MP4 to MP3 (from a folder with mp4 files)

import moviepy.editor as mp
import re
tgt_folder = "/folder/contains/your/mp4"

for file in [n for n in os.listdir(tgt_folder) if'mp4',n)]:
full_path = os.path.join(tgt_folder, file)
output_path = os.path.join(tgt_folder, os.path.splitext(file)[0] + '.mp3')
clip = mp.AudioFileClip(full_path).subclip(10,) # disable if do not want any clipping

Custom Contour Plots with Labelled points

Creating Customized Contour Plots with Labelled Points

I was asked to create a customized contour plot based on a chart (Fig 1 ) found in IEEE Transactions on Magnetics journal with some variant in requirements. The chart shows the areal density capacity (ADC) demo of certain samples on a bit density (BPI) by track density (TPI) chart. The two different contours shown in the plot are made up of ADC (BPI * TPI) and bit aspect ratio BAR (BPI/TPI).

A way to create the plot might be to generate the contours based on Excel and manually added in the different points. This proves to be too much work. Therefore, a simpler way is needed. Further requirements include having additional points (with labels) to be added in fairly easily and charts with different sets of data can be recreated rapidly.

Creating the Contours

The idea will be to use the regression plots for both the ADC and the BAR contours while the points and labels can be automatically added to the plots after reading from an Excel table (or csv file). The regression plots are based on seaborn lmplot and the points with labels are annotated on the chart based on the individual x, and y values.

Besides the seaborn, pandas, matplotlib and numpy,  additional module adjustText is used to prevent overlapping of the text labels in the plot

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from adjustText import adjust_text

## Create GridLines for the ADC GBPSI
ADC_tgt = range(650,2150,50)
BPI_tgt = list(range(800,2700,20))*3
data_list = [ [ADC, BPI, ADC*1000/BPI] for BPI in BPI_tgt for ADC in ADC_tgt]
ADC_df = pd.DataFrame(data_list, columns=['Contour','X','Y']) #['ADC','TPI','BPI']
ADC_df['Contour'] = ADC_df['Contour'].astype('category')

## Create GridLines for the BAR
BAR_tgt =[1.0,1.5,2.0, 2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.5]
BPI_tgt = list(range(800,2700,20))*3
data_list = [ [BAR, BPI, BPI/BAR] for BPI in BPI_tgt for BAR in BAR_tgt]
BAR_df = pd.DataFrame(data_list, columns=['Contour','X','Y']) #['BAR','TPI','BPI']
BAR_df['Contour'] = BAR_df['Contour'].astype('category')

combined_df = pd.concat([ADC_df,BAR_df])

Adding the demo points with text from Excel

The various points are updated in the excel sheet (or csv) , shown in fig 2, and read using pandas. Two data frames are produced, pts_df and text_df which is the dataframe from the points and the associated text. These, together with the contour data frame from above, are then feed into the seaborn lmplot. Note the points shown in the Excel and plots are randomly generated.

class ADC_DataPts():

    def __init__(self, xls_fname, header_psn = 0):
        self.xls_fname = xls_fname
        self.header_psn = header_psn
        self.data_df = pd.read_excel(self.xls_fname, header = self.header_psn)

    def generate_pts_text_df(self):
        pts_df = self.data_df['X Y Color'.split()]
        text_df = self.data_df['X_TxtPsn Y_TxtPsn TextContent'.split()]
        return pts_df, text_df

data_excel = r"yourexcelpath.xls"
adc_data = ADC_DataPts(data_excel, header_psn =1)
pts_df, text_df = adc_data.generate_pts_text_df()

Seaborn lmplot

The seaborn lmplot is used for the contours while the points are individually annotated on the graph

def generate_contour_plots_with_points(xlabel, ylabel, title):

    # overall settings for plots
    sns.set_style("whitegrid", \
                  {'grid.linestyle': ':', 'xtick.bottom': True, 'xtick.direction': 'out',\
                    'xtick.color': '.15','axes.grid' : False}

    # Generate the different "contour"
    g = sns.lmplot("X", "Y", data=combined_df, hue='Contour', order =2, \
               height =7, aspect =1.5, ci =False, line_kws={'color':'0.9', 'linestyle':':'}, \
                scatter=False, legend_out =False)

    # Bold the key contour lines
    for n in [1.0,2.0,3.0]:
        sub_bar = BAR_df[BAR_df['Contour']==n]
        #generate the bar contour, x= "X", y="Y", data=sub_bar ,scatter= False, ci =False, \
              line_kws={'color':'0.9', 'linestyle':'-', 'alpha':0.05, 'linewidth':'3'})

    for n in [1000,1500,2000]:
        sub_adc = ADC_df[ADC_df['Contour']==n]
        #generate the bar contour, x= "X", y="Y", data=sub_adc ,scatter= False, ci =False, order =2, \
              line_kws={'color':'0.9', 'linestyle':'-', 'alpha':0.05, 'linewidth':'3'})#'color':'0.7', 'linestyle':'-', 'alpha':0.05, 'linewidth':'2'

    # Generate the different points
    for index, rows in pts_df.iterrows():
        g = g.map_dataframe(plt.plot, rows['X'], rows['Y'], 'o',  color = rows['Color'])# generate plot with differnt color or use annotation?

    ax = g.axes.flat[0]    

    # text annotation on points
    style = dict(size=12, color='black', verticalalignment='top')
    txt_grp = []
    for index, rows in text_df.iterrows():
        txt_grp.append(ax.text( rows['X_TxtPsn'], rows['Y_TxtPsn'], rows['TextContent'], **style) )#how to find space, separate data base

    style2 = dict(size=12, color='grey', verticalalignment='top')
    style3 = dict(size=12, color='grey', verticalalignment='top', rotation=30, alpha= 0.7)

    # Label the key contours
    ax.text( 2400, 430, '1000 Gfpsi', **style2)
    ax.text( 2400, 640, '1500 Gfpsi', **style2)
    ax.text( 2400, 840, '2000 Gfpsi', **style2) 

    ax.text( 1100, 570, 'BAR 2.0', **style3)
    ax.text( 1300, 460, 'BAR 3.0', **style3) 

    # Set x y limit

    # Set general plot attributes

    adjust_text(txt_grp, x = pts_df.X.tolist() , y = pts_df.Y.tolist() , autoalign = True, expand_points=(1.4, 1.4))

generate_contour_plots_with_points('kBPI', 'kTPI', "DEMO Areal Density Capability\n")


Fig 1: Sample plot from Heat-Assisted Interlaced Magnetic Recording IEEE Vol 54 No2


Fig2: Excel tables with associated demo points, the respective color and the text labels


Fig 3: Generated chart with the ADC and BAR contours and demo pts with labels

Heap Map for discrepancy check

Monitoring counts discrepancy

In one aspect of my work, we have a group of samples undergoing several rounds of modifications with same set of tests being performed at each round. For each test, parameters for each sample are collected. For some samples, a particular test may fail in certain rounds resulting in no/missing parameters being collected for that test.

When we compare the performance of the samples especially grouping as a mean, missing parameters from certain samples at certain rounds may skew the results. To ensure accuracy, we need to ensure matching samples data. As there are multiple tests and few hundreds parameters being tracked, we need a way to keep track of the parameters that have mismatch parameters between rounds.

A simple way will be to use the heat map to highlight parameters that have discrepancy in number of counts (this will mean that some samples are missing in data) between rounds. The script is generated using mainly Pandas and Seaborn.


  1. Group the counts for each parameter for each round.
  2. Use one round as reference (default 1st round), take the differences in counts for each parameter for each round.
  3. Display as heat map for only rounds that have discrepancy.
import os, sys, datetime, re
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# retrieve zone data
rawfile = 'raw_data.csv'
raw_df = pd.read_csv(rawfile)

# count of data in group
cnt_df = raw_df.groupby(['round']).count()

# Substract the first to the rest
diff_df = cnt_df.subtract(cnt_df.iloc[0], axis = 1)

# drop columns where it is all zeros, meaning exclude data that are matched.
diff_df.loc[:, diff_df.any()]

fig, ax = plt.subplots(figsize=(10,10))  

sns.heatmap(diff_df.loc[:, diff_df.any()].T,  xticklabels=True, yticklabels=True, ax =ax , annot=True, fmt="d", center= 0 ,  cmap="coolwarm")