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titan | Distributed Graph Database | Graph Database library

 by   thinkaurelius Java Version: Current License: Apache-2.0

 by   thinkaurelius Java Version: Current License: Apache-2.0

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kandi X-RAY | titan Summary

titan is a Java library typically used in Database, Graph Database applications. titan has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub, Maven.
Distributed Graph Database
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • titan has a highly active ecosystem.
  • It has 5134 star(s) with 1045 fork(s). There are 414 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 157 open issues and 968 have been closed. On average issues are closed in 141 days. There are 35 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of titan is current.
titan Support
Best in #Graph Database
Average in #Graph Database
titan Support
Best in #Graph Database
Average in #Graph Database

quality kandi Quality

  • titan has 0 bugs and 0 code smells.
titan Quality
Best in #Graph Database
Average in #Graph Database
titan Quality
Best in #Graph Database
Average in #Graph Database

securitySecurity

  • titan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • titan code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
titan Security
Best in #Graph Database
Average in #Graph Database
titan Security
Best in #Graph Database
Average in #Graph Database

license License

  • titan is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
titan License
Best in #Graph Database
Average in #Graph Database
titan License
Best in #Graph Database
Average in #Graph Database

buildReuse

  • titan releases are not available. You will need to build from source code and install.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
titan Reuse
Best in #Graph Database
Average in #Graph Database
titan Reuse
Best in #Graph Database
Average in #Graph Database
Top functions reviewed by kandi - BETA

kandi has reviewed titan and discovered the below as its top functions. This is intended to give you an instant insight into titan implemented functionality, and help decide if they suit your requirements.

  • Performs a query and returns the entries .
  • Returns a list of vertex IDs corresponding to the given vertex .
  • Returns the given column name padded with zeros .
  • Marks the entry and sweep .
  • Creates a buffer with positive length
  • Builds a query filter for a given condition .
  • Gets the unique id block block .
  • Reads a vertex with the given key .
  • Convert a list of constraints to a QNF constraints .
  • Constructs a query from a result type .

titan Key Features

Distributed Graph Database

How do I add within-group percentages as bar labels on a seaborn count plot?

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df = sns.load_dataset('titanic')

feat = 'sex'
hue = 'survived'
hue_type = df[hue].dtype.type

groups = df[feat].unique()
proportions = df.groupby(feat)[hue].value_counts(normalize=True)
# sex     survived
# female  1           0.742038
#         0           0.257962
# male    0           0.811092
#         1           0.188908
# Name: survived, dtype: float64

ax = sns.countplot(x=feat, hue=hue, data=df)

for c in ax.containers:
    labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
    # proportions.loc['male', 0] => 0.811092
    # proportions.loc['male', 1] => 0.188908
    # proportions.loc['female', 0] => 0.257962
    # proportions.loc['female', 1] => 0.742038

    ax.bar_label(c, labels)
titanic = sns.load_dataset('titanic')

features = ['sex', 'class', 'who', 'adult_male']
hue = 'survived'
hue_type = df[hue].dtype.type

fig, axs = plt.subplots(2, 2, figsize=(10, 10), constrained_layout=True)

for feat, ax in zip(features, axs.ravel()):
    # group ordering differs by dtype
    col = titanic[feat]
    if col.dtype == 'category':
        groups = col.cat.categories
    elif col.dtype == 'bool':
        groups = [False, True]
    else:
        groups = col.unique()

    # within-feature proportions
    proportions = titanic.groupby(feat)[hue].value_counts(normalize=True)
    
    sns.countplot(x=feat, hue=hue, edgecolor='k', alpha=0.8, data=titanic, ax=ax)
    ax.set_title(f'Countplot of ${feat}$ by ${hue}$')
    sns.despine()

    # retrieve proportions by the container's label (hue) and group (feature)
    for c in ax.containers:
        labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
        ax.bar_label(c, labels)
-----------------------
df = sns.load_dataset('titanic')

feat = 'sex'
hue = 'survived'
hue_type = df[hue].dtype.type

groups = df[feat].unique()
proportions = df.groupby(feat)[hue].value_counts(normalize=True)
# sex     survived
# female  1           0.742038
#         0           0.257962
# male    0           0.811092
#         1           0.188908
# Name: survived, dtype: float64

ax = sns.countplot(x=feat, hue=hue, data=df)

for c in ax.containers:
    labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
    # proportions.loc['male', 0] => 0.811092
    # proportions.loc['male', 1] => 0.188908
    # proportions.loc['female', 0] => 0.257962
    # proportions.loc['female', 1] => 0.742038

    ax.bar_label(c, labels)
titanic = sns.load_dataset('titanic')

features = ['sex', 'class', 'who', 'adult_male']
hue = 'survived'
hue_type = df[hue].dtype.type

fig, axs = plt.subplots(2, 2, figsize=(10, 10), constrained_layout=True)

for feat, ax in zip(features, axs.ravel()):
    # group ordering differs by dtype
    col = titanic[feat]
    if col.dtype == 'category':
        groups = col.cat.categories
    elif col.dtype == 'bool':
        groups = [False, True]
    else:
        groups = col.unique()

    # within-feature proportions
    proportions = titanic.groupby(feat)[hue].value_counts(normalize=True)
    
    sns.countplot(x=feat, hue=hue, edgecolor='k', alpha=0.8, data=titanic, ax=ax)
    ax.set_title(f'Countplot of ${feat}$ by ${hue}$')
    sns.despine()

    # retrieve proportions by the container's label (hue) and group (feature)
    for c in ax.containers:
        labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
        ax.bar_label(c, labels)

Why is my SQL Database not recognizing my 2nd and 3rd tables as relational tables, despite recognizing the 1st?

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CREATE DATABASE equipment;
CREATE USER
  'equipment'@'localhost' IDENTIFIED WITH mysql_native_password
                                   BY 'testing';
GRANT ALL PRIVILEGES ON equipment.* to equipment@localhost;

use equipment;

CREATE TABLE material (
          material_id     INTEGER NOT NULL,
          name            VARCHAR(128) NOT NULL,
          description     VARCHAR(256) NOT NULL,
          tensile_strength INTEGER NOT NULL,
          PRIMARY KEY     ( material_id )
);

CREATE TABLE costs (
          cost_id        INTEGER NOT NULL,
          material_id    INTEGER NOT NULL,
          price          DECIMAL(10,2) NOT NULL,
          CONSTRAINT fk_material_id FOREIGN KEY (material_id) REFERENCES material(material_id),
          PRIMARY KEY     ( cost_id )
);

CREATE TABLE usages (
          usage_id         INTEGER NOT NULL,
          cost_id          INTEGER NOT NULL,
          purpose          VARCHAR(256) NOT NULL,
          location_to_purchase  VARCHAR(256) NOT NULL,
          CONSTRAINT fk_cost_id FOREIGN KEY (cost_id) REFERENCES costs(cost_id),
          PRIMARY KEY ( usage_id )

);



INSERT INTO material VALUES (0, 'Steel', 'Steel is the highest quality material used on barbells and weight plates in the gym. It has the highest durability, and as such, is typically the most expensive', '205000');
INSERT INTO material VALUES (1, 'Cast Iron', 'Cast Iron is of a notably lesser quality, but is significantly more affordable for gym equipment owners. It is frequently found in all-in-one training equipment sets for young adults', '120000');
INSERT INTO material VALUES (2, 'Bronze', 'Bronze is a material used much less frequently than steel or cast iron, but it is much more attainable and can be used in making custom gym equipment, due to its high affordability', '85000');
INSERT INTO costs VALUES (0, 0, '449.52');
INSERT INTO costs VALUES (0, 1, '262.73');
INSERT INTO costs VALUES (0, 2, '160.41');
INSERT INTO usages VALUES (0, 0, 'Used for extremely heavy loads on the barbell with minimal stress exerted on the bar shaft', 'Can be purchased at Rogue Fitness, EliteFTS, Titan Fitness, and small competitors as well');
INSERT INTO usages VALUES (1, 1, 'Used for light to moderate loads. Frequently found in beginner training where technique training is the priority. It is also easily replaceable', 'Can be found on Rogue Fitness, Amazon, and re-sell marketplaces such as Craigslist');
INSERT INTO usages VALUES (2, 2, 'Used in adolescent training. Bars can be cut to size to be a best fit for the user, and it is even more obtainable than steel or iron since you can buy bronze in bulk', 'Not found on many fitness marketplaces, but can be acquired on material wholesale locations such as the Online Metals website');
CREATE DATABASE equipment;
CREATE USER equipment WITH ENCRYPTED PASSWORD 'testing';
GRANT ALL PRIVILEGES ON DATABASE equipment TO equipment;
use equipment;

CREATE TABLE material (
          material_id     INTEGER NOT NULL,
          name            VARCHAR(128) NOT NULL,
          description     VARCHAR(256) NOT NULL,
          tensile_strength INTEGER NOT NULL,
          PRIMARY KEY     ( material_id )
);

CREATE TABLE costs (
          cost_id        INTEGER NOT NULL,
          material_id    INTEGER NOT NULL,
          price          DECIMAL(10,2) NOT NULL,
          CONSTRAINT fk_material_id FOREIGN KEY (material_id) REFERENCES material(material_id),
          PRIMARY KEY     ( cost_id )
);

CREATE TABLE usages (
          usage_id         INTEGER NOT NULL,
          cost_id          INTEGER NOT NULL,
          purpose          VARCHAR(256) NOT NULL,
          location_to_purchase  VARCHAR(256) NOT NULL,
          CONSTRAINT fk_cost_id FOREIGN KEY (cost_id) REFERENCES costs(cost_id),
          PRIMARY KEY ( usage_id )

);

GRANT ALL PRIVILEGES ON material, costs, usages TO equipment;

INSERT INTO material VALUES (0, 'Steel', 'Steel is the highest quality material used on barbells and weight plates in the gym. It has the highest durability, and as such, is typically the most expensive', '205000');
INSERT INTO material VALUES (1, 'Cast Iron', 'Cast Iron is of a notably lesser quality, but is significantly more affordable for gym equipment owners. It is frequently found in all-in-one training equipment sets for young adults', '120000');
INSERT INTO material VALUES (2, 'Bronze', 'Bronze is a material used much less frequently than steel or cast iron, but it is much more attainable and can be used in making custom gym equipment, due to its high affordability', '85000');
INSERT INTO costs VALUES (0, 0, '449.52');
INSERT INTO costs VALUES (1, 1, '262.73');
INSERT INTO costs VALUES (2, 2, '160.41');
INSERT INTO usages VALUES (0, 0, 'Used for extremely heavy loads on the barbell with minimal stress exerted on the bar shaft', 'Can be purchased at Rogue Fitness, EliteFTS, Titan Fitness, and small competitors as well');
INSERT INTO usages VALUES (1, 1, 'Used for light to moderate loads. Frequently found in beginner training where technique training is the priority. It is also easily replaceable', 'Can be found on Rogue Fitness, Amazon, and re-sell marketplaces such as Craigslist');
INSERT INTO usages VALUES (2, 2, 'Used in adolescent training. Bars can be cut to size to be a best fit for the user, and it is even more obtainable than steel or iron since you can buy bronze in bulk', 'Not found on many fitness marketplaces, but can be acquired on material wholesale locations such as the Online Metals website');
-----------------------
CREATE DATABASE equipment;
CREATE USER
  'equipment'@'localhost' IDENTIFIED WITH mysql_native_password
                                   BY 'testing';
GRANT ALL PRIVILEGES ON equipment.* to equipment@localhost;

use equipment;

CREATE TABLE material (
          material_id     INTEGER NOT NULL,
          name            VARCHAR(128) NOT NULL,
          description     VARCHAR(256) NOT NULL,
          tensile_strength INTEGER NOT NULL,
          PRIMARY KEY     ( material_id )
);

CREATE TABLE costs (
          cost_id        INTEGER NOT NULL,
          material_id    INTEGER NOT NULL,
          price          DECIMAL(10,2) NOT NULL,
          CONSTRAINT fk_material_id FOREIGN KEY (material_id) REFERENCES material(material_id),
          PRIMARY KEY     ( cost_id )
);

CREATE TABLE usages (
          usage_id         INTEGER NOT NULL,
          cost_id          INTEGER NOT NULL,
          purpose          VARCHAR(256) NOT NULL,
          location_to_purchase  VARCHAR(256) NOT NULL,
          CONSTRAINT fk_cost_id FOREIGN KEY (cost_id) REFERENCES costs(cost_id),
          PRIMARY KEY ( usage_id )

);



INSERT INTO material VALUES (0, 'Steel', 'Steel is the highest quality material used on barbells and weight plates in the gym. It has the highest durability, and as such, is typically the most expensive', '205000');
INSERT INTO material VALUES (1, 'Cast Iron', 'Cast Iron is of a notably lesser quality, but is significantly more affordable for gym equipment owners. It is frequently found in all-in-one training equipment sets for young adults', '120000');
INSERT INTO material VALUES (2, 'Bronze', 'Bronze is a material used much less frequently than steel or cast iron, but it is much more attainable and can be used in making custom gym equipment, due to its high affordability', '85000');
INSERT INTO costs VALUES (0, 0, '449.52');
INSERT INTO costs VALUES (0, 1, '262.73');
INSERT INTO costs VALUES (0, 2, '160.41');
INSERT INTO usages VALUES (0, 0, 'Used for extremely heavy loads on the barbell with minimal stress exerted on the bar shaft', 'Can be purchased at Rogue Fitness, EliteFTS, Titan Fitness, and small competitors as well');
INSERT INTO usages VALUES (1, 1, 'Used for light to moderate loads. Frequently found in beginner training where technique training is the priority. It is also easily replaceable', 'Can be found on Rogue Fitness, Amazon, and re-sell marketplaces such as Craigslist');
INSERT INTO usages VALUES (2, 2, 'Used in adolescent training. Bars can be cut to size to be a best fit for the user, and it is even more obtainable than steel or iron since you can buy bronze in bulk', 'Not found on many fitness marketplaces, but can be acquired on material wholesale locations such as the Online Metals website');
CREATE DATABASE equipment;
CREATE USER equipment WITH ENCRYPTED PASSWORD 'testing';
GRANT ALL PRIVILEGES ON DATABASE equipment TO equipment;
use equipment;

CREATE TABLE material (
          material_id     INTEGER NOT NULL,
          name            VARCHAR(128) NOT NULL,
          description     VARCHAR(256) NOT NULL,
          tensile_strength INTEGER NOT NULL,
          PRIMARY KEY     ( material_id )
);

CREATE TABLE costs (
          cost_id        INTEGER NOT NULL,
          material_id    INTEGER NOT NULL,
          price          DECIMAL(10,2) NOT NULL,
          CONSTRAINT fk_material_id FOREIGN KEY (material_id) REFERENCES material(material_id),
          PRIMARY KEY     ( cost_id )
);

CREATE TABLE usages (
          usage_id         INTEGER NOT NULL,
          cost_id          INTEGER NOT NULL,
          purpose          VARCHAR(256) NOT NULL,
          location_to_purchase  VARCHAR(256) NOT NULL,
          CONSTRAINT fk_cost_id FOREIGN KEY (cost_id) REFERENCES costs(cost_id),
          PRIMARY KEY ( usage_id )

);

GRANT ALL PRIVILEGES ON material, costs, usages TO equipment;

INSERT INTO material VALUES (0, 'Steel', 'Steel is the highest quality material used on barbells and weight plates in the gym. It has the highest durability, and as such, is typically the most expensive', '205000');
INSERT INTO material VALUES (1, 'Cast Iron', 'Cast Iron is of a notably lesser quality, but is significantly more affordable for gym equipment owners. It is frequently found in all-in-one training equipment sets for young adults', '120000');
INSERT INTO material VALUES (2, 'Bronze', 'Bronze is a material used much less frequently than steel or cast iron, but it is much more attainable and can be used in making custom gym equipment, due to its high affordability', '85000');
INSERT INTO costs VALUES (0, 0, '449.52');
INSERT INTO costs VALUES (1, 1, '262.73');
INSERT INTO costs VALUES (2, 2, '160.41');
INSERT INTO usages VALUES (0, 0, 'Used for extremely heavy loads on the barbell with minimal stress exerted on the bar shaft', 'Can be purchased at Rogue Fitness, EliteFTS, Titan Fitness, and small competitors as well');
INSERT INTO usages VALUES (1, 1, 'Used for light to moderate loads. Frequently found in beginner training where technique training is the priority. It is also easily replaceable', 'Can be found on Rogue Fitness, Amazon, and re-sell marketplaces such as Craigslist');
INSERT INTO usages VALUES (2, 2, 'Used in adolescent training. Bars can be cut to size to be a best fit for the user, and it is even more obtainable than steel or iron since you can buy bronze in bulk', 'Not found on many fitness marketplaces, but can be acquired on material wholesale locations such as the Online Metals website');

pd.get_dummies() not converting categorical data to one hot encoded vectors when multiple features are used

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from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({'Pclass': [0, 1, 2], 'SibSp': [3, 1, 0],
                   'Parch': [0, 2, 2], 'Sex': [0, 1, 1]})

ohe = OneHotEncoder()
data = ohe.fit_transform(df[['Pclass', 'SibSp', 'Parch', 'Sex']])
df1 = pd.DataFrame(data.toarray(), columns=ohe.get_feature_names_out(), dtype=int)
>>> df
   Pclass  SibSp  Parch  Sex
0       0      3      0    0
1       1      1      2    1
2       2      0      2    1

>>> df1
   Pclass_0  Pclass_1  Pclass_2  SibSp_0  SibSp_1  SibSp_3  Parch_0  Parch_2  Sex_0  Sex_1
0         1         0         0        0        0        1        1        0      1      0
1         0         1         0        0        1        0        0        1      0      1
2         0         0         1        1        0        0        0        1      0      1
-----------------------
from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({'Pclass': [0, 1, 2], 'SibSp': [3, 1, 0],
                   'Parch': [0, 2, 2], 'Sex': [0, 1, 1]})

ohe = OneHotEncoder()
data = ohe.fit_transform(df[['Pclass', 'SibSp', 'Parch', 'Sex']])
df1 = pd.DataFrame(data.toarray(), columns=ohe.get_feature_names_out(), dtype=int)
>>> df
   Pclass  SibSp  Parch  Sex
0       0      3      0    0
1       1      1      2    1
2       2      0      2    1

>>> df1
   Pclass_0  Pclass_1  Pclass_2  SibSp_0  SibSp_1  SibSp_3  Parch_0  Parch_2  Sex_0  Sex_1
0         1         0         0        0        0        1        1        0      1      0
1         0         1         0        0        1        0        0        1      0      1
2         0         0         1        1        0        0        0        1      0      1

What does the error "single-row subquery returns more than one row" means and how to fix it?

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SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MAX(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MIN(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > ALL (SELECT SCORE FROM MOVIE
SELECT TITLE FROM MOVIE
WHERE SCORE > ANY (SELECT SCORE FROM MOVIE)
-----------------------
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MAX(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MIN(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > ALL (SELECT SCORE FROM MOVIE
SELECT TITLE FROM MOVIE
WHERE SCORE > ANY (SELECT SCORE FROM MOVIE)
-----------------------
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MAX(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MIN(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > ALL (SELECT SCORE FROM MOVIE
SELECT TITLE FROM MOVIE
WHERE SCORE > ANY (SELECT SCORE FROM MOVIE)
-----------------------
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MAX(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > (SELECT MIN(SCORE) FROM MOVIE)
SELECT TITLE FROM MOVIE
WHERE SCORE > ALL (SELECT SCORE FROM MOVIE
SELECT TITLE FROM MOVIE
WHERE SCORE > ANY (SELECT SCORE FROM MOVIE)
-----------------------
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT SCORE
                FROM   MOVIE
                ORDER BY DBMS_RANDOM.VALUE()
                FETCH FIRST ROW ONLY
               );
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT score
                FROM   (
                  SELECT SCORE
                  FROM   MOVIE
                  ORDER BY DBMS_RANDOM.VALUE()
                )
                WHERE  ROWNUM = 1
               );
SELECT title
FROM   (
  SELECT title,
         score,
         MIN(score)
           KEEP (DENSE_RANK FIRST ORDER BY DBMS_RANDOM.VALUE(), ROWNUM)
           OVER ()
           AS random_score
  FROM   MOVIE
)
WHERE  score > random_score;
-----------------------
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT SCORE
                FROM   MOVIE
                ORDER BY DBMS_RANDOM.VALUE()
                FETCH FIRST ROW ONLY
               );
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT score
                FROM   (
                  SELECT SCORE
                  FROM   MOVIE
                  ORDER BY DBMS_RANDOM.VALUE()
                )
                WHERE  ROWNUM = 1
               );
SELECT title
FROM   (
  SELECT title,
         score,
         MIN(score)
           KEEP (DENSE_RANK FIRST ORDER BY DBMS_RANDOM.VALUE(), ROWNUM)
           OVER ()
           AS random_score
  FROM   MOVIE
)
WHERE  score > random_score;
-----------------------
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT SCORE
                FROM   MOVIE
                ORDER BY DBMS_RANDOM.VALUE()
                FETCH FIRST ROW ONLY
               );
SELECT TITLE
FROM   MOVIE
WHERE  SCORE > (SELECT score
                FROM   (
                  SELECT SCORE
                  FROM   MOVIE
                  ORDER BY DBMS_RANDOM.VALUE()
                )
                WHERE  ROWNUM = 1
               );
SELECT title
FROM   (
  SELECT title,
         score,
         MIN(score)
           KEEP (DENSE_RANK FIRST ORDER BY DBMS_RANDOM.VALUE(), ROWNUM)
           OVER ()
           AS random_score
  FROM   MOVIE
)
WHERE  score > random_score;

Is there an alternative way to fit Poisson model in R besides using glm?

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glm(as.numeric(survived=="no") ~ class, family = poisson, data = titanic)
cc <- as.data.frame(table(titanic))
glm(Freq ~ ., data = cc, family = poisson)
-----------------------
glm(as.numeric(survived=="no") ~ class, family = poisson, data = titanic)
cc <- as.data.frame(table(titanic))
glm(Freq ~ ., data = cc, family = poisson)

Regex ensure that there's only one blank line(2 newlines) after each section ends and another section begins in any Text-INI file

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(\n){2,}(?!\n*\[[^\][]*\]$)

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). df[condition]

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condition = (df[‘embark_town’] == 'Southampton')
df[condition]['embark_town'] = 'Manchester'
def change(series):
  if series.name == 'embark_town':
      series[series.values == 'Southampton'] = 'Manchester'

  return series
-----------------------
condition = (df[‘embark_town’] == 'Southampton')
df[condition]['embark_town'] = 'Manchester'
def change(series):
  if series.name == 'embark_town':
      series[series.values == 'Southampton'] = 'Manchester'

  return series

TypeError: Cannot index by location index with a non-integer key in for loop

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>>> df['class'].map({'First': '01', 'Second': '02', 'Third': '03'})
0      03
1      01
2      03
3      01
4      03
       ..
886    02
887    01
888    03
889    01
890    03
Name: class, Length: 891, dtype: category
Categories (3, object): ['01', '02', '03']

Flexible placement of labels in seaborn barplots

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fig, ax = plt.subplots()
sns.countplot(
    y='sex',
    data=titanic,
    orient="h",
    ax=ax
    )

values = []
for p in ax.patches:
    
    x = p.get_width() # counts; dimension of the bar
    y = p.get_y() + p.get_height() / 2
    values.append(x) #collect the bar sizes in a list
    x_max = max(values)   #determine the largest bar size
    
#adjust percentage of the maximum bar size where inside/outside annotation occurs
cut_off = 0.8*x_max

for p in ax.patches:
    perc = "{:.1f}%".format(100 * p.get_width() / titanic.shape[0]) #the label for the bar
    x = p.get_width()
    y = p.get_y() + p.get_height() / 2
    
    if x >= cut_off:
        ax.annotate(perc, (x*0.8, y), color="white") 
    else: 
        ax.annotate(perc, (x*1.1, y), color="black")

How can I change a Panda Dataframe so that the values are strictly between 0 and 1 while preserving the NaNs?

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df_m[df_m.eq(0)] = 0.0001
print(df_m.T)

# Output
         C       Q       A     All
0    0.500  0.3720  0.3020  0.3430
1    0.657  0.3750  0.4380  0.5060
2    0.750  0.6670  0.3750  0.4780
3      NaN     NaN  0.3640  0.3640
4      NaN  0.0001  0.3000  0.2140
5      NaN     NaN  0.0001  0.0001
8      NaN     NaN  0.0001  0.0001
All  0.556  0.3620  0.3360  0.3770
df_m = df_m.clip(lower=0.001, upper=0.999)
-----------------------
df_m[df_m.eq(0)] = 0.0001
print(df_m.T)

# Output
         C       Q       A     All
0    0.500  0.3720  0.3020  0.3430
1    0.657  0.3750  0.4380  0.5060
2    0.750  0.6670  0.3750  0.4780
3      NaN     NaN  0.3640  0.3640
4      NaN  0.0001  0.3000  0.2140
5      NaN     NaN  0.0001  0.0001
8      NaN     NaN  0.0001  0.0001
All  0.556  0.3620  0.3360  0.3770
df_m = df_m.clip(lower=0.001, upper=0.999)

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QUESTION

How do I add within-group percentages as bar labels on a seaborn count plot?

Asked 2022-Mar-26 at 04:15

My goal is to create barplots with counts on the y axis and have the bars labelled with the percentage of the group. The code below gets me halfway there -

import seaborn as sns
from itertools import product
titanic = sns.load_dataset("titanic")

features = ['sex', 'class', 'who', 'adult_male']
n = 1

plt.figure(figsize=[12, 14])

for f in features:
    plt.subplot(3, 2, n)
    ax = sns.countplot(x=f, hue='survived', edgecolor='black', alpha=0.8, data=titanic)
    sns.despine()
    plt.title("Countplot of {}  by alive".format(f))
    n=n+1
    plt.tight_layout()
    
    for c in ax.containers:
        labels = [f'{h/titanic.survived.count()*100:0.1f}%' if (h := v.get_height()) > 0 else '' for v in c]
        ax.bar_label(c,labels=labels, label_type='edge')

enter image description here

The problem is that the percentages incorrect. For example in the "Countplot of sex by survived" chart, male percentage is calculating the percentage of males in the "0" class out of the entire dataset.

How do I adjust my code to calculate the percentage of males in the "0" class out of male category? So the blue bar in the male category should be 81% and the orange bar should be 19%.

ANSWER

Answered 2022-Mar-26 at 04:15

Generate within-feature proportions manually, e.g. for feature sex:

  1. Compute the proportions of survived within each sex using groupby.value_counts()
  2. Access a given bar's proportion via its group (male/female sex) and label (0/1 survived)
    • The groups will be ordered differently per dtype, so unique() won't always work (see the full example in the next section)
    • The label of a container c is c.get_label(), which can be converted to the appropriate type via df[hue].dtype.type
df = sns.load_dataset('titanic')

feat = 'sex'
hue = 'survived'
hue_type = df[hue].dtype.type

groups = df[feat].unique()
proportions = df.groupby(feat)[hue].value_counts(normalize=True)
# sex     survived
# female  1           0.742038
#         0           0.257962
# male    0           0.811092
#         1           0.188908
# Name: survived, dtype: float64

ax = sns.countplot(x=feat, hue=hue, data=df)

for c in ax.containers:
    labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
    # proportions.loc['male', 0] => 0.811092
    # proportions.loc['male', 1] => 0.188908
    # proportions.loc['female', 0] => 0.257962
    # proportions.loc['female', 1] => 0.742038

    ax.bar_label(c, labels)


Full example with all features:

titanic = sns.load_dataset('titanic')

features = ['sex', 'class', 'who', 'adult_male']
hue = 'survived'
hue_type = df[hue].dtype.type

fig, axs = plt.subplots(2, 2, figsize=(10, 10), constrained_layout=True)

for feat, ax in zip(features, axs.ravel()):
    # group ordering differs by dtype
    col = titanic[feat]
    if col.dtype == 'category':
        groups = col.cat.categories
    elif col.dtype == 'bool':
        groups = [False, True]
    else:
        groups = col.unique()

    # within-feature proportions
    proportions = titanic.groupby(feat)[hue].value_counts(normalize=True)
    
    sns.countplot(x=feat, hue=hue, edgecolor='k', alpha=0.8, data=titanic, ax=ax)
    ax.set_title(f'Countplot of ${feat}$ by ${hue}$')
    sns.despine()

    # retrieve proportions by the container's label (hue) and group (feature)
    for c in ax.containers:
        labels = [f'{proportions.loc[g, hue_type(c.get_label())]:.1%}' for g in groups]
        ax.bar_label(c, labels)

corrected figure

Source https://stackoverflow.com/questions/71549867

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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