Shantanu's Blog

Corporate Consultant

January 30, 2020

 

Google translate API

# This will translate from Finnish text into Marathi using high level python module that uses google API internally.

import googletrans
translator = googletrans.Translator()
result = translator.translate("Mikä on nimesi", src="fi", dest="mr")
result.text
# 'तुझे नाव काय आहे'
# What is your name

# googletrans.LANGUAGES # for full list of supported languages

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January 26, 2020

 

Copy mysql data to another server using tar and nc

If you want to copy the data from one server for e.g. (63) to another (64):

1) Stop MySQL service on both servers, for e.g. 10.10.10.63 and 10.10.10.64
2) Go to /var/lib/mysql/ directory on both servers.
cd /var/lib/mysql/

3) On 10.10.10.63
tar -cf - * | nc -l 1234

4) On 10.10.10.64
nc 10.10.10.63 1234 | tar xf -

Restart MySQL service on both the servers and you should get exactly the same data on 64 as you see on 63 (assuming you have the same my.cnf config). This needs to be done very carefully or else data may corrupt.

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January 24, 2020

 

distro-less using multi-stage

"Distroless" images contain only your application and its runtime dependencies. They do not contain package managers, shells or any other programs you would expect to find in a standard Linux distribution. Docker multi-stage builds make using distroless images easy.

# vi Dockerfile

FROM python:3-slim AS build-env
ADD . /app
WORKDIR /app

FROM gcr.io/distroless/python3
COPY --from=build-env /app /app
WORKDIR /app
CMD ["hello.py", "/etc"]


# Build and run the image as usual

docker build -t myapp .
docker run -t myapp

More info:
https://github.com/GoogleContainerTools/distroless

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January 23, 2020

 

Ensemble explained in plain words

Bagging classification method like Random Forests Classifier, will train all the individual trees on a different sample of the dataset. The tree is also trained using random selections of features. When the results are averaged together, the overall variance decreases and the model performs better as a result.

Boosting algorithms like adaboost or gradient boosting are capable of taking weak, underperforming models and converting them into strong models. You assign many weak learning models to the datasets, and then the weights for misclassified examples are tweaked during subsequent rounds of learning. The predictions of the classifiers are aggregated and then the final predictions are made through a weighted sum (in the case of regressions), or a weighted majority vote (in the case of classification).

https://stackabuse.com/ensemble-voting-classification-in-python-with-scikit-learn/

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Pandas case study 21

I have 2 dataframes having exactly the same data, but in a different order and with different column names.
Based on the numbers in the two data frames, I would like to be able to match each column name in df1 to each column name in df2.

from io import StringIO
import pandas as pd

audit_trail1 = StringIO(
    """a1   a2   a3   a4   a5   a6   a7
1    3    4    5    3    4    5
0    2    0    3    0    2    1
2    5    6    5    2    1    2
"""
)

df1 = pd.read_csv(audit_trail1, sep="\s+")


audit_trail2 = StringIO(
    """b1   b2   b3   b4   b5   b6   b7
3    5    4    5    1    4    3
0    1    2    3    0    0    2
2    2    1    5    2    6    5
"""
)

df2 = pd.read_csv(audit_trail2, sep="\s+")

# Solution:

m = df1.T.sort_values(by=list(df1.index)).index
n = df2.T.sort_values(by=list(df2.index)).index
dict(zip(m, n))

# https://stackoverflow.com/questions/59721120/use-data-in-pandas-data-frames-to-match-columns-together

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January 22, 2020

 

Understanding Naive Bayes

Naive Bayes implicitly assumes that all the attributes are mutually independent. That is not the case in most of the cases. If a categorical variable has a category in the test dataset, which was not observed in training dataset, then the model may fail.

Naive Bayes can handle any type of data (for e.g. continuous or discrete) and the size of data does not really matter. It can be applied to IRIS dataset as shown below:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
gnb = GaussianNB()
y_pred = gnb.fit(X_train, y_train).predict(X_test)
accuracy_score(y_test, y_pred)

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Pandas case study 20

As per this blog post, there are 5 methods to select data from pandas dataframe.

https://kanoki.org/2020/01/21/pandas-dataframe-filter-with-multiple-conditions/#comment-39

Let's use the same example as shown in the post:

df = pd.DataFrame(
    {
        "Name": ["JOHN", "ALLEN", "BOB", "NIKI", "CHARLIE", "CHANG"],
        "Age": [35, 42, 63, 29, 47, 51],
        "Salary_in_1000": [100, 93, 78, 120, 64, 115],
        "FT_Team": [
            "STEELERS",
            "SEAHAWKS",
            "FALCONS",
            "FALCONS",
            "PATRIOTS",
            "STEELERS",
        ],
    }
)

# 1) use loc method

df.loc[
    (df["Salary_in_1000"] >= 100)
    & (df["Age"] < 60)
    & (df["FT_Team"].str.startswith("S")),
    ["Name", "Age", "Salary_in_1000"],
]


# 2) Use numpy where

idx = np.where(
    (df["Salary_in_1000"] >= 100)
    & (df["Age"] < 60)
    & (df["FT_Team"].str.startswith("S"))
)

df.iloc[idx[0], :3]


# 3) use query method

df.query('Salary_in_1000 >= 100 &  Age < 60 & FT_Team.str.startswith("S").values')[
    ["Name", "Age", "Salary_in_1000"]
]


# 4) use boolean indexing

df[
    (df["Salary_in_1000"] >= 100) & (df["Age"] < 60) & df["FT_Team"].str.startswith("S")
][["Name", "Age", "Salary_in_1000"]]


# 5) use eval method

df[df.eval("Salary_in_1000>=100 &  (Age < 60) & FT_Team.str.startswith('S').values")][
    ["Name", "Age", "Salary_in_1000"]
]

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