Related
I want to store collected data in json, they are all arrays of integers with couple of thousand elements each.
I want the file to have the elements in the same list to be in the same line, should look like
{
"foo": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
"bar": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]
}
Right now, all I can find json in python can only be export with either
{'foo': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ... , 'bar': [0 ...]}
Or
{
"foo": [
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
],
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
But neither is desirable.
Using jsbeautifier:
import json
import jsbeautifier
x = {'foo': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0]], 'bar': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
options = jsbeautifier.default_options()
options.indent_size = 2
print(jsbeautifier.beautify(json.dumps(x), options))
OUTPUT:
{
"foo": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
"bar": [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]
}
I have two data frames here: review and negative_word(there is one column with some words)
I choose a column review['Review Text'] of review, then I want to count how many times all words from negative_word for each row of review['Review Text'].
Actually I use a word(like"wonderful" to test it, it works.
But when I choose all word in the data frame with for loop, it shows all 0.
Here is my code:
count_neg = []
for i in negative_word:
for j in range(len(review)):
count = review['Review Text'][j].count(i)
count_neg.append(count)
print(count_neg)
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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For a DataFrame review, you can create a function to capture only the negative words in a string and return the count. This should be faster than a loop or creating a number of DataFrames, and definitely more readable.
import string
import pandas as pd
# example dataframe
review = pd.DataFrame({'Item': ['Book A', 'Movie B', 'Restaurant C'],
'Review Text': ["It was great, I couldn't put it down.",
"It was horribly boring.",
"The food was delicious but the service was bad."]})
review
Item Review Text
0 Book A It was great, I couldn't put it down.
1 Movie B It was horribly boring.
2 Restaurant C The food was delicious but the service was bad.
I'm using a list of words here -- if yours is stored as a 1-column DataFrame you can use the df['column'].tolist() method to get it into a list.
# example list of negative words
negative_word = ['bad', 'horrible', 'worst', 'hate', 'boring', '...more words...']
def bad_count(review):
"""Return the number of words from negative list in review text"""
# strip punctuation
review = review.strip(string.punctuation)
# convert to lowercase & separate words
review = review.lower().split(' ')
# get list of review words contained in negative word list
bad = [word for word in review if word in negative_word]
# return length of list
return len(bad)
Now apply the function to the review text column:
review['Count of Negative Words'] = review['Review Text'].map(bad_count)
review
Item Review Text Count of Negative Words
0 Book A It was great, I couldn't put it down. 0
1 Movie B It was horribly boring. 2
2 Restaurant C The food was delicious but the service was bad. 1
So if you're not concerned about memory (i.e. you have a manageable number of words) you can use the following. If not you will probably have to use a loop. Happy to update my answer if that is the case
import pandas as pd
import numpy as np
# Data frame
df = pd.DataFrame({'col1':[['a', 'b', 'c', 'c', 'd'], ['c', 'c', 'b', 'x', 'x'], ['x', 'x', 'y', 'y', 'y']]})
# Negative series
neg = pd.Series(['x', 'y', 'z'])
# Create a number of columns equal to the vocabulary size with their counts
df = pd.concat([df, df['col1'].apply(lambda x: pd.Series(x).value_counts())], axis=1)
# From that dataframe get the columns that intersect with values in negative and take the sum
df['neg_count'] = df[df.columns.intersection(neg)].sum(axis=1)
df.head()
I have a dataframe that consists of rows like the following. My goal here is to compute the the cosine similarity of every row with every row within the same category, such that I'd end up with a dataframe with 3 columns: category, vecs, and dist where dist is a n length array that contains the distance between each row and every row within the same category.
category vecs
0 a [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...]
1 a [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...]
2 b [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...]
3 b [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...]
The inefficient solution that I've though of would be to loop through each row, check if cat is equal and then compute distance and add to list else continue loop. This solution would be n^2 though and I'm looking for something more efficient. I have 8115 rows in this dataframe and am looking for something that would possibly scale to even larger datasets.
The other possible solution I've looked at would be using sklearn pairwise distance (metric = cosine) and somehow only include computations with same categories, but I'm struggling to think about how to do this.
Would someone be willing to help or suggest a different efficient solution?
You need to do the (more or less) n(n-1)/2 computations.
This is irreducible, since the similarities have to be computed somehow if there is no hidden structure in the vectors.
You can use scipy to compute the pairwise distances, and the squareform function to get back a regular symmetric matrix, that would otherwise be the triangular flattened:
from scipy.spatial.distance import pdist, squareform
similarities = dict()
for cat, group in df.groupby("category"):
a = tuple(row.vecs for _, row in group.iterrows())
b = np.array(a)
sim_mat = squareform(1 - pdist(b, metric='cosine'))
similarities[cat] = sim_mat
[print(k, v, sep='\n') for k, v in similarities.items()]
a
[[0. 1.]
[1. 0.]]
b
[[0. 0.70710678]
[0.70710678 0. ]]
So me and my friend have been working on recreating Conway's game of life in python. It's been going well until we started drawing the cells with pygame. we just finished the grid easily but for some unknown reason our cells won't draw.
Please let us know whats wrong with the draw command.
Here's the code:
#Import goodies
import time
import pygame
pygame.init()
pygame.display.init()
#Create dimension variables
width = 32
height = 18
rectwidth = 1280 / width
rectheight = 720 / height
#Colors!
run = True
white = (255, 255, 255)
black = (0, 0, 0)
#Set up screen for pygame
size = (1280, 720)
screen = pygame.display.set_mode(size)
pygame.display.set_caption("Conway's Game of Life")
#Build frames
initial_frame = [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,]]
next_frame = [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,]]
row = []
#Create neighbor variable
neighborcount = 0
#While run loop to prevent lag after closing window
while run:
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
#Time for the big loop!
while True:
#Drawing grid
for b in range(0, width + 1):
pygame.draw.rect(screen, white, pygame.Rect(b * 40, 720, 1, -720))
for c in range(0, height + 1):
pygame.draw.rect(screen, white, pygame.Rect(1280, c * 40, -1280, 1))
pygame.display.flip()
#Start nested for loop for neighbor checking
for i in range(1, width):
for o in range(1, height):
#Check all 8 neighbors:
neighborcount = 0
#Down 1
if initial_frame[(o + 1)][i] == 1:
neighborcount += 1
#Up 1
if initial_frame[(o - 1)][i] == 1:
neighborcount += 1
#Right 1
if initial_frame[o][(i + 1)] == 1:
neighborcount += 1
#Left 1
if initial_frame[o][(i - 1)] == 1:
neighborcount += 1
#Down 1, Right 1
if initial_frame[(o + 1)][(i + 1)] == 1:
neighborcount += 1
#Down 1, Left 1
if initial_frame[(o + 1)][(i - 1)] == 1:
neighborcount += 1
#Up 1, Left 1
if initial_frame[(o - 1)][(i - 1)] == 1:
neighborcount += 1
#Up 1, Right 1
if initial_frame[(o - 1)][(i + 1)] == 1:
neighborcount += 1
#If dead cell has exactly 3 neighbors, set it to be born
next_frame[o][i] = initial_frame[o][i]
if initial_frame[o][i] == 0 and neighborcount == 3:
next_frame[o][i] = 1
#If living cell:
if initial_frame[o][i] == 1:
#draw it!
pygame.draw.rect(screen, white, pygame.Rect(o * 40, -i * 40, rectwidth, rectheight))
#If it does not have either 2 or 3 neighbors, set it to die
if neighborcount != 2 and neighborcount != 3:
next_frame[o][i] = 0
#reset neighbors
neighborcount = 0
#Project set values onto real board
garbage_arr = initial_frame
initial_frame = next_frame
next_frame = garbage_arr
#Finished!
pygame.quit()
In
pygame.draw.rect(screen, white, pygame.Rect(o * 40, -i * 40, rectwidth, rectheight))
the variabel i is negated.
This causes that the y coordinate of the rectangle is negative and the rectangle is out of bounds of the surface.
Change -i to i, to solve the issue:
pygame.draw.rect(screen, white, pygame.Rect(o * 40, i * 40, rectwidth, rectheight))
This is my code to
count number of times a word occurred in a file( All entries are in Unicode)
Text_file = open("Mytext.txt", 'r').read()
Wordlist = {'മാന്നാര്':[], 'മാന്':[]}
for line in Text_file:
for word in Wordlist.keys():
Wordlist[word].append(line.count(word))
My expected result is
'മാന്നാര്' _ 5
മാന് _ 1
My_text =
കുരുവികളോട് കൂട്ട് കൂടാന് … മട്ടാഞ്ചേരി കുരുവികളോടൊത്ത് കൂട്ടുകൂടാനും സംരക്ഷിക്കുവാനും കുരുന്നുമനസ്സുകളില് ബോധമുണര്ത്താന് ജെയിന് ഫൗണ്ടേഷന് രംഗത്ത് ലോക കുരുവി ദിനമായ ഇന്നലെ കുരുന്നുകള്ക്ക് കുരുവിക്കൂടും കുടിവെള്ളപാത്രവും നല്കിക്കൊണ്ടാണ് ഫൗണ്ടേഷന് പക്ഷി-മൃഗാദി പരിശീലന പദ്ധതി നടപ്പിലാക്കുന്നത് സ്ക്കൂളുകള് ലൈബ്രറികള് എന്നിവ കേന്ദ്രീകരിച്ചാണ് ഫൗണ്ടേഷന് പദ്ധതി നടപ്പിലാക്കുന്നത് കുരുവികളെ സംരക്ഷിക്കുന്നതിനും പരിചരിക്കുന്നതിനുമായി പരിസ്ഥിതി സൗഹൃദമായ മണ്കുടങ്ങളാണ് ഫൗണ്ടേഷന് സമ്മാനിച്ചത് വേനല്കാല ചൂടില് ദാഹമകറ്റുന്നതിന് മണ്കലങ്ങളും ഇതിനോടൊപ്പം നല്കുകയും ചെയ്തു
ലോകകുരുവി ദിനത്തില് നടന്ന കുരുവികള്ക്ക് കൂടൊരുക്കാം പരിപാടിയില് വിദേശികളും സ്വദേശികളും സാക്ഷികളായി ഫോര്ട്ടുകൊച്ചിയിലെ സെന്റ് മാര്ക്കസ് സ്ക്കൂളിലെ കുട്ടികള്ക്കാണ് ഫൗണ്ടേഷന് കുരുവിക്കൂടുകള് നല്കിയത് ജൈന് ഫൗണ്ടേഷന് ജനമൈത്രി പോലീസ് സെന്റ്മാര്ക്കസ് സ്ക്കൂള് എന്നിവരുമായി കൈകോര്ത്ത് സംഘടിപ്പിച്ച പരിപാടിയില് ജനമൈത്രി പോലീസ് സി ആര് ഒ പി യു ഹരിദാസ് സ്ക്കൂള് പ്രിന്സിപ്പല് ഹേറിന് ഫെര്ണാണ്ടസിന് നല്കി പദ്ധതി ഉദ്ഘാടനം ചെയ്തു ഫൗണ്ടേഷന് ഭാരവാഹി മുകേഷ് ജെയിന് ശാന്തി മേനോന് പ്രിയ കെനറ്റ് എം എം സലീം സുധി എന്നിവര് സംസാരിച്ചു
But I am getting
{'\xe0\xb4\xae\xe0\xb4\xbe\xe0\xb4\xa8\xe0\xb5\x8d\xe0\xb4\xa8\xe0\xb4\xbe\xe0\xb4\xb0\xe0\xb5\x8d\xe2\x80\x8d': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'മാന്': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
What is the error here ?
You need your script file to be unicode, and you need python to open the input file as unicode, utf-8, utf-16 - whatever is the encoding of your file. For example,
import codecs
f = codecs.open('Mytext.txt', encoding='utf-16')
for line in f:
print repr(line)
See http://docs.python.org/2/howto/unicode.html
Apart from that you need your dictionary to map the counted strings to the count, not to a list, as in,
Wordlist = {'മാന്നാര്':0, 'മാന്':0}
When you need to increment the dictionary entry:
Wordlist['മാന്നാര്'] += 1