pokemon_data | 全ポケモンのJSONデータです。 | Video Game library

by kotofurumiya JavaScript Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(9)Vulnerabilities Install Support

kandi X-RAY | pokemon_data Summary

pokemon_data is a JavaScript library typically used in Gaming, Video Game applications. pokemon_data has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

全ポケモンのJSONデータです。

Support

Quality

Security

License

Reuse

Support

pokemon_data has a low active ecosystem.

It has 202 star(s) with 24 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 6 have been closed. On average issues are closed in 123 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pokemon_data is current.

Quality

pokemon_data has 0 bugs and 0 code smells.

Security

pokemon_data has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pokemon_data code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pokemon_data does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

pokemon_data releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of pokemon_data

Get all kandi verified functions for this library.

pokemon_data Key Features

No Key Features are available at this moment for pokemon_data.

pokemon_data Examples and Code Snippets

No Code Snippets are available at this moment for pokemon_data.

Community Discussions

Trending Discussions on pokemon_data

How to Pivot Columns of a Pandas DataFrame into Inner-most Level Index without Using df.iterrows()?

I started getting to know pandas via a youtube-tutorial. After comleting the tutorial i encountered the following problem:

I can't subset rows in pandas this way: df[0] (or with any integer)

Tensorflow InvalidArgumentError: assertion failed: [Labels must be <= n_classes - 1]

UnimplementedError: Cast string to float is not supported while using Tensorflow

How to make a menu bar used in filter in qtablewidget be scrollable in pyqt5?

Python DataFrames concat or append problem

TypeError: can only concatenate str (not "list") to str - pandas

I tried to import a data set from text file i got an Nameerror

QUESTION

How to Pivot Columns of a Pandas DataFrame into Inner-most Level Index without Using df.iterrows()?

Asked 2021-Aug-09 at 08:57

The original .csv file -

...

ANSWER

Answered 2021-Aug-09 at 08:57

With str() you can get the string representation of each row, then concatenate them together with .str.cat:

Source https://stackoverflow.com/questions/68704710

QUESTION

I started getting to know pandas via a youtube-tutorial. After comleting the tutorial i encountered the following problem:

Asked 2021-Apr-09 at 17:03

I was working with the exact same csv-file the tutorial provided. While doing the tutorial i did not encounter the error. when trying to build onto my knowledge i acquired during the tutorial i got the following error, althought i did not change the data in the file, or anything with the code, if you can even call it code. Searched the internet for similar errors, but did not find anything.

The code:

...

ANSWER

Answered 2021-Apr-09 at 17:03

df.columns will return an Index like this. You can think of it as a list of the names of the columns:

Index(['Size', 'Speed', 'RPM'], dtype='object')

You could extract an element from the Index, but it would just return a string:

df.columns[1] would return Speed.

Probably, what you want is to actually get a column from the dataframe! You could do this with

df['Speed'] or df.Speed - not by going through the df.columns attribute.

Source https://stackoverflow.com/questions/67025287

QUESTION

I can't subset rows in pandas this way: df[0] (or with any integer)

Asked 2021-Jan-06 at 18:40

I loaded a csv and then tried to get the first row with the row index number

...

ANSWER

Answered 2021-Jan-06 at 18:36

pkm[0] calls for the column named 0 in pkm. That's why it's not working. Try pkm['HP'] or using a column name and it will be clear.

Source https://stackoverflow.com/questions/65601380

QUESTION

Tensorflow InvalidArgumentError: assertion failed: [Labels must be <= n_classes - 1]

Asked 2021-Jan-03 at 02:45

import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
df = pd.read_csv('pokemon_data.csv')

df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']

df = df.loc[df['Total'] > 450]
df = df.loc[~df['Name'].str.contains('Mega')]
df = df.loc[~df['Name'].str.contains('Primal')]

df = df.drop(columns = ['Name'])
df = df.drop(columns = ['Generation'])
df = df.drop(columns = ['Legendary'])
df = df.drop(columns = ['Type 2'])
df = df.drop(columns = ['#'])

df.loc[df['Type 1'] == 'Fire', 'Type 1'] = 0
df.loc[df['Type 1'] == 'Normal', 'Type 1'] = 1
df.loc[df['Type 1'] == 'Water', 'Type 1'] = 2
df.loc[df['Type 1'] == 'Grass', 'Type 1'] = 3
df.loc[df['Type 1'] == 'Electric', 'Type 1'] = 4
df.loc[df['Type 1'] == 'Ice', 'Type 1'] = 5
df.loc[df['Type 1'] == 'Fighting', 'Type 1'] = 6
df.loc[df['Type 1'] == 'Poison', 'Type 1'] = 7
df.loc[df['Type 1'] == 'Ground', 'Type 1'] = 8
df.loc[df['Type 1'] == 'Flying', 'Type 1'] = 9
df.loc[df['Type 1'] == 'Psychic', 'Type 1'] = 10
df.loc[df['Type 1'] == 'Rock', 'Type 1'] = 11
df.loc[df['Type 1'] == 'Bug', 'Type 1'] = 12
df.loc[df['Type 1'] == 'Ghost', 'Type 1'] = 13
df.loc[df['Type 1'] == 'Dark', 'Type 1'] = 14
df.loc[df['Type 1'] == 'Dragon', 'Type 1'] = 15
df.loc[df['Type 1'] == 'Steel', 'Type 1'] = 16
df.loc[df['Type 1'] == 'Fairy', 'Type 1'] = 17

TEMP = ['Type 1']
for col in TEMP:
    df[col] = pd.to_numeric(df[col])

df_eval_sub = df.loc[df['Total'] < 500]
df_eval_over = df.loc[df['Total'] > 500]                      
y_train = df.pop('Type 1')
y_eval_sub = df_eval_sub.pop('Type 1')
y_eval_over = df_eval_over.pop('Type 1')                     
                      
feature_columns = []

TO_INT = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Total']
for col in TO_INT:
    df[col] = pd.to_numeric(df[col])


NUMERIC_COLUMNS = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Total']
for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))

def make_input_fn(data_df, label_df, num_epochs = 10, shuffle = True, batch_size=32):
    def input_function():
        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
        if shuffle:
            ds = ds.shuffle(1000)
        ds = ds.batch(batch_size).repeat(num_epochs)
        return ds
    return input_function

train_input_fn = make_input_fn(df, y_train)
eval_input_fn = make_input_fn(df_eval_sub, y_eval_sub, num_epochs = 1, shuffle = False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)
result = linear_est.evaluate(eval_input_fn)

...

ANSWER

Answered 2021-Jan-03 at 02:45

The n_classes it's not means the number of columns, it's the parameter in tf.estimator.LinearClassifier that you have to specify, and the classes in your labels must <= n_classes, in your case you should set linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns, n_classes=18)

Source https://stackoverflow.com/questions/65545303

QUESTION

UnimplementedError: Cast string to float is not supported while using Tensorflow

Asked 2021-Jan-02 at 23:03

import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
df = pd.read_csv('pokemon_data.csv')
df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']
df = df.loc[df['Total'] > 450]
df = df.loc[~df['Name'].str.contains('Mega')]
df = df.loc[~df['Name'].str.contains('Primal')]
df = df.drop(columns = ['Name'])
df = df.drop(columns = ['Generation'])
df = df.drop(columns = ['Legendary'])
df = df.drop(columns = ['Type 2'])
df = df.drop(columns = ['#'])
df_eval_sub = df.loc[df['Total'] < 500]
df_eval_over = df.loc[df['Total'] > 500]                      
y_train = df.pop('Type 1')
y_eval_sub = df_eval_sub.pop('Type 1')
y_eval_over = df_eval_over.pop('Type 1')                     
                      
feature_columns = []

NUMERIC_COLUMNS = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Total']
for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
    
def make_input_fn(data_df, label_df, num_epochs = 10, shuffle = True, batch_size=32):
    def input_function():
        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
        if shuffle:
            ds = ds.shuffle(1000)
        ds = ds.batch(batch_size).repeat(num_epochs)
        return ds
    return input_function

train_input_fn = make_input_fn(df, y_train)
eval_input_fn = make_input_fn(df_eval_sub, y_eval_sub, num_epochs = 1, shuffle = False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)
result = linear_est.evaluate(eval_input_fn)

clear_output()
print(result['accuracy'])

...

ANSWER

Answered 2021-Jan-02 at 23:03

I can reproduce your error by using a tf.feature_column.numeric_column on a dataframe column that has string values.

Source https://stackoverflow.com/questions/65544296

QUESTION

How to make a menu bar used in filter in qtablewidget be scrollable in pyqt5?

Asked 2020-Jun-07 at 15:40

I am really new to pyqt and this question maybe silly. Any help, I would really appreciate. I have this code I get from this page where it codes in filtering a qtablewidget. This code works perfectly fine to my desired filtering output. However I have a table and it has lot of rows, I want the menu bar used in filtering to be scrollable instead of displaying all unique contents in the row. I want to have fixed size height of the menubar.

This is the code:

...

ANSWER

Answered 2020-Jun-07 at 15:40

The easiest solution is to use an undocumented stylesheet property (as proposed in the unaccepted answer of this post).

Source https://stackoverflow.com/questions/62246492

QUESTION

Python DataFrames concat or append problem

Asked 2020-May-22 at 10:20

I have a problem with dataframes in Python. I am trying to copy certain rows to a new dataframe but I can't figure it out.

There are 2 arrays:

...

ANSWER

Answered 2020-Apr-25 at 14:07

For this type of wrangling, you should first "melt" or "tidy" the combats_data so each ID has its own row, then do a "join" or "merge" of the two dataframes.

You didn't provide a minimum reproducible example, so here's mine:

Source https://stackoverflow.com/questions/61425595

QUESTION

TypeError: can only concatenate str (not "list") to str - pandas

Asked 2020-Mar-29 at 20:09

I'm working on a personal project, and I need to delete some rows from my dataframe, and the easiest way I found is to move a column to the position 0, then remove the rows whose 'headline' contain certain values.

...

ANSWER

Answered 2020-Mar-29 at 20:09

Use:

Source https://stackoverflow.com/questions/60920048

QUESTION

I tried to import a data set from text file i got an Nameerror

Asked 2020-Mar-16 at 18:05

I just started learning pandas and when I tried to import a data set from a text file I got a Name error and both files in the same folder this is my the code

...

ANSWER

Answered 2020-Mar-16 at 16:26

You have to write pokemon_data.csv in quotation marks like df = pd.read_csv("pokemon_data.csv").

Source https://stackoverflow.com/questions/60709641

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pokemon_data

You can download it from GitHub.

Support

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 .

Find more information at: