<jupyter_start><jupyter_text>Data visualization in python  Importing the data [Pandas](https://pandas.pydata.org) provides excellent data reading and querying module, [dataframe](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html), which allows you to import structured data and perform SQL-like queries.Here we imported some house price records from Trulia. For more about extracting data from [Trulia](https://www.trulia.com/?cid=sem|google|tbw_br_nat_x_x_nat!53f9be4f|Trulia-Exact_352364665_22475209465_aud-278383240986:kwd-1967776155_260498918114_), please check [my previous tutorial](https://www.youtube.com/watch?v=qB418v3k2vk).<jupyter_code>import pandas
df = pandas.read_excel('house_price.xlsx')
df[:10]
df.describe()
%matplotlib inline

df.hist(figsize = (20,20))
from pandas.plotting import scatter_matrix
scatter_matrix(df, figsize = (20,20), diagonal = 'kde')
df.groupby('house_type').count()
df.groupby('house_type').mean()
df.groupby('house_type').hist(figsize=(12,4),column=['price','views'])
df.plot.scatter(x='built_in',y='price')
df['unit_price']=df['price']/df['area']
mean_price =df.groupby('built_in').mean()
mean_price.plot.line(y='unit_price')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15146579804560262, 'fraction_numerical': 0.06188925081433225, 'mean_word_length': 8.453846153846154, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 4, 'lorem ipsum': 0, 'www.': 2, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '116049', 'license': 'MIT', 'n_tokens_mistral': 494, 'n_tokens_neox': 445, 'n_words': 73, 'path': 'lab2.ipynb', 'repo_name': 'owainscholbe/ia480-real-', 'size': '1228'}