AayushSameerShah

Simple creating views

-- General Syntax
CREATE VIEW view_name AS
<SELECT QUERY>

Alter viewes

CREATE OR REPLACE VIEW view_name AS 

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There are 2 types of CASE:

1. General CASE
2. CASE expression

General Syntax

-- General Syntax
CASE
    WHEN condition1 THEN result1
 WHEN condition2 THEN result2

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1. There are 2 constraints which might help

1. CEHCK
2. EXCLUDES

-- CHECK
CREATE TABLE table_a
(

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SELECT *
FROM table_A
FULL OUTER JOIN table_B
  ON table_A.col = table_B.col
WHERE table_A.col IS null
OR table_B.col IS null

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tokens = nltk.word_tokenize(text.lower())
text = nltk.Text(tokens)
tags = nltk.pos_tag(text)

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handles_0, labels_0 = axes[0].get_legend_handles_labels()
handles_1, labels_1 = axes[1].get_legend_handles_labels()

handels = handles_0 + handles_1
labels = labels_0 + labels_1
fig.legend(handels, labels, loc=(0.83, 0.85))

"""Here, the main function is `.get_legend_handles_labels()` 
it will return the labels and handles for individual plots

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from sklearn import set_config
set_config(display='diagram')
pipeline

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def new_ft(operation):
    ft = {}
    for i in range(len(num_features)):
        for j in range(i+1, len(num_features)):
            ft1 = num_features[i]
            ft2 = num_features[j]
            oparated = eval(f"combined_xy[ft1] {operation} combined_xy[ft2]")
            corr = oparated.corr(combined_xy["co2"])
            ft[f"{ft1} {operation} {ft2}"] = corr
    ser = pd.Series(ft)

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# For most plots
plt.legend([], [], frameon=False);

# For pairplot
temp = sns.pairplot(...)
temp._legend.remove()

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plt.figure(figsize=(10, 6))
corr = df.corr()
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True

sns.heatmap(corr, mask=mask, square=True, cmap="Blues")