<jupyter_start><jupyter_text>Sciagniecie danych<jupyter_code>!pip install datadotworld
!pip install datadotworld[pandas]

!dw configure
from google.colab import drive
import pandas as pd
import numpy as np

import datadotworld as dw
drive.mount("/content/drive")
ls
cd '/content/drive/My Drive/Colab Notebooks/dw_matrix'
ls
!mkdir data
!echo 'data' > .gitignore
!git add .gitignore
data = dw.load_dataset('datafiniti/mens-shoe-prices')
df = data.dataframes['7004_1']
df.shape
df.columns
df.prices_currency.unique()
df['prices_currency'].value_counts(normalize = True)
df_usd = df[df['prices_currency'] == 'USD'].copy()
df_usd.shape
df_usd.prices_amountmin.head()
df_usd['prices_amountmin'] = df_usd.prices_amountmin.astype(np.float)
df_usd['prices_amountmin'].hist()<jupyter_output><empty_output><jupyter_text>Radzenie sobie z wartościami odstającymi<jupyter_code>filter_max = np.percentile(df_usd['prices_amountmin'],99)
filter_max
df_usd_filter = df_usd[ df_usd['prices_amountmin'] < filter_max ]
df_usd_filter['prices_amountmin'].hist(bins=100);
ls
ls matrix1<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12545787545787546, 'fraction_numerical': 0.010073260073260074, 'mean_word_length': 8.504347826086956, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 10, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '428225', 'license': 'MIT', 'n_tokens_mistral': 442, 'n_tokens_neox': 430, 'n_words': 71, 'path': 'matrix1/Day3.ipynb', 'repo_name': 'KL2020/dw_matrix', 'size': '1092'}