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IIS/assets/eda/eda.ipynb

443 KiB
Исходник Вина История 


import pandas as pd
import matplotlib as plt
import seaborn as sns
import numpy as np
import pickle

Загрузка и знакомство с данными

# dataset https://www.kaggle.com/datasets/mrdaniilak/russia-real-estate-20182021/data 

df = pd.read_csv('data/all_v2.csv')
df.head(10)
price date time geo_lat geo_lon region building_type level levels rooms area kitchen_area object_type
0 6050000 2018-02-19 20:00:21 59.805808 30.376141 2661 1 8 10 3 82.6 10.8 1
1 8650000 2018-02-27 12:04:54 55.683807 37.297405 81 3 5 24 2 69.1 12.0 1
2 4000000 2018-02-28 15:44:00 56.295250 44.061637 2871 1 5 9 3 66.0 10.0 1
3 1850000 2018-03-01 11:24:52 44.996132 39.074783 2843 4 12 16 2 38.0 5.0 11
4 5450000 2018-03-01 17:42:43 55.918767 37.984642 81 3 13 14 2 60.0 10.0 1
5 3300000 2018-03-02 21:18:42 55.908253 37.726448 81 1 4 5 1 32.0 6.0 1
6 4704280 2018-03-04 12:35:25 55.621097 37.431002 3 2 1 25 1 31.7 6.0 11
7 3600000 2018-03-04 20:52:38 59.875526 30.395457 2661 1 2 5 1 31.1 6.0 1
8 3390000 2018-03-05 07:07:05 53.195031 50.106952 3106 2 4 24 2 64.0 13.0 11
9 2800000 2018-03-06 09:57:10 55.736972 38.846457 81 1 9 10 2 55.0 8.0 1 
df.info(show_counts=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5477006 entries, 0 to 5477005
Data columns (total 13 columns):
 #   Column         Non-Null Count    Dtype  
---  ------         --------------    -----  
 0   price          5477006 non-null  int64  
 1   date           5477006 non-null  object 
 2   time           5477006 non-null  object 
 3   geo_lat        5477006 non-null  float64
 4   geo_lon        5477006 non-null  float64
 5   region         5477006 non-null  int64  
 6   building_type  5477006 non-null  int64  
 7   level          5477006 non-null  int64  
 8   levels         5477006 non-null  int64  
 9   rooms          5477006 non-null  int64  
 10  area           5477006 non-null  float64
 11  kitchen_area   5477006 non-null  float64
 12  object_type    5477006 non-null  int64  
dtypes: float64(4), int64(7), object(2)
memory usage: 543.2+ MB

df['region'] = df['region'].astype('category')
df['building_type'] = df['building_type'].astype('category')
df['object_type'] = df['object_type'].astype('category')
df['level'] = df['level'].astype('int8')
df['levels'] = df['levels'].astype('int8')
df['rooms'] = df['rooms'].astype('int8')
df['rooms'] = df['rooms'].astype('int8')

df['area'] = df['area'].astype('float16')
df['kitchen_area'] = df['kitchen_area'].astype('float16')
df['kitchen_area'] = df['kitchen_area'].astype('float16')
df['geo_lat'] = df['geo_lat'].astype('float32')
df['geo_lon'] = df['geo_lon'].astype('float32')
df.describe()
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/pandas/core/nanops.py:1487: RuntimeWarning: overflow encountered in cast
  return dtype.type(n)
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/numpy/_core/_methods.py:53: RuntimeWarning: overflow encountered in reduce
  return umr_sum(a, axis, dtype, out, keepdims, initial, where)
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/pandas/core/nanops.py:731: RuntimeWarning: invalid value encountered in scalar divide
  the_mean = the_sum / count if count > 0 else np.nan
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/pandas/core/nanops.py:1487: RuntimeWarning: overflow encountered in cast
  return dtype.type(n)
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/numpy/_core/_methods.py:53: RuntimeWarning: overflow encountered in reduce
  return umr_sum(a, axis, dtype, out, keepdims, initial, where)
/home/andrey/work/institute/MLE/assets/eda/.venv_eda/lib/python3.10/site-packages/pandas/core/nanops.py:731: RuntimeWarning: invalid value encountered in scalar divide
  the_mean = the_sum / count if count > 0 else np.nan
price geo_lat geo_lon level levels rooms area kitchen_area
count 5.477006e+06 5.477006e+06 5.477006e+06 5.477006e+06 5.477006e+06 5.477006e+06 5.477006e+06 5.477006e+06
mean 4.422029e+06 5.403825e+01 5.324431e+01 6.214530e+00 1.139892e+01 1.726173e+00 NaN NaN
std 2.150752e+07 4.622758e+00 2.074763e+01 4.957419e+00 6.535734e+00 1.082133e+00 0.000000e+00 0.000000e+00
min -2.144967e+09 4.145906e+01 1.989020e+01 1.000000e+00 1.000000e+00 -2.000000e+00 7.000732e-02 1.000214e-02
25% 1.950000e+06 5.337767e+01 3.777790e+01 2.000000e+00 5.000000e+00 1.000000e+00 3.800000e+01 7.000000e+00
50% 2.990000e+06 5.517139e+01 4.306774e+01 5.000000e+00 1.000000e+01 2.000000e+00 4.803125e+01 9.703125e+00
75% 4.802000e+06 5.622613e+01 6.564895e+01 9.000000e+00 1.600000e+01 2.000000e+00 6.312500e+01 1.270312e+01
max 2.147484e+09 7.198040e+01 1.625361e+02 3.900000e+01 3.900000e+01 1.000000e+01 7.856000e+03 1.000000e+04 
cat_features = df.select_dtypes(include=['category']).columns.to_list()
cat_features
['region', 'building_type', 'object_type']
num_features = df.select_dtypes(include=['number']).columns.to_list()
num_features
['price',
 'geo_lat',
 'geo_lon',
 'level',
 'levels',
 'rooms',
 'area',
 'kitchen_area']
df = df.drop(columns=['date', 'time'])
df.info(show_counts=True) # уменьшили размер в 3 раза (135 Мб против 543)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5477006 entries, 0 to 5477005
Data columns (total 11 columns):
 #   Column         Non-Null Count    Dtype   
---  ------         --------------    -----   
 0   price          5477006 non-null  int64   
 1   geo_lat        5477006 non-null  float32 
 2   geo_lon        5477006 non-null  float32 
 3   region         5477006 non-null  category
 4   building_type  5477006 non-null  category
 5   level          5477006 non-null  int8    
 6   levels         5477006 non-null  int8    
 7   rooms          5477006 non-null  int8    
 8   area           5477006 non-null  float16 
 9   kitchen_area   5477006 non-null  float16 
 10  object_type    5477006 non-null  category
dtypes: category(3), float16(2), float32(2), int64(1), int8(3)
memory usage: 135.8 MB

for cat in cat_features:
    print(f'{cat} - numer of unique = {df[cat].nunique()}')
region - numer of unique = 84
building_type - numer of unique = 6
object_type - numer of unique = 2

for col in cat_features:
    print(f'Unique categories in {col}: {df[col].value_counts()}')
Unique categories in region: region
9654     1049435
2843      637224
81        500368
2661      461820
3         439511
          ...   
16705        139
69            77
4963          65
1901          12
61888          5
Name: count, Length: 84, dtype: int64
Unique categories in building_type: building_type
1    1955661
3    1892756
2    1130731
0     307165
4     174356
5      16337
Name: count, dtype: int64
Unique categories in object_type: object_type
1     3863809
11    1613197
Name: count, dtype: int64

Очистка данных

df = df.loc[df['price'] > 50000]
df = df.query('area >= 8')
df = df.query('kitchen_area >= 3')
df = df.query('kitchen_area < area')
df = df.query('rooms != -2')
df = df.query('level <= levels')
# меняем признак "студии" -1 на 0
df.loc[df['rooms'] == -1, 'rooms'] = 0

Анализ признаков для модели

https://seaborn.pydata.org/examples/index.html - галерея примеров

displot

sns.displot(df, x='price', bins=100, hue='building_type', kde=True, log_scale=True)
<seaborn.axisgrid.FacetGrid at 0x7df3e5371c00>

# Создаем новый признак - уровень высоты этажа в здании
df['floor_level'] = df.apply(lambda x: 'low' if x['level']/x['levels'] <= 0.3 \
                             else 'hi' if x['level']/x['levels'] > 0.7 \
                             else 'mid', axis = 1) 
df['floor_level'] = df.apply(lambda x: 'first' if x['level'] == 1 \
                            else 'last' if x['level'] == x['levels'] \
                            else x['floor_level'], axis = 1)
df['floor_level'].value_counts()
floor_level
mid      2092890
hi       1208629
low       740915
last      697099
first     654013
Name: count, dtype: int64
sns.displot(df, x='price', bins=100, hue='floor_level', kde=True, log_scale=True)
<seaborn.axisgrid.FacetGrid at 0x7df3e55f4be0>

heatmap

feature_correlation = df[num_features].corr()
sns.heatmap(feature_correlation, annot=True)
<Axes: >

Групповые операции

def flat_index(df_stats): 
    df_stats.columns = df_stats.columns.get_level_values(0) + '_' +  df_stats.columns.get_level_values(1) 
    df_stats.columns = df_stats.columns.to_flat_index() 
    df_stats.reset_index(inplace=True) 
    return df_stats
aggregated_df = df[num_features+['building_type']].groupby(by='building_type').agg(['mean', 'std'])
aggregated_df = flat_index(aggregated_df)
aggregated_df 
/tmp/ipykernel_1524625/3084051676.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  aggregated_df = df[num_features+['building_type']].groupby(by='building_type').agg(['mean', 'std'])
building_type price_mean price_std geo_lat_mean geo_lat_std geo_lon_mean geo_lon_std level_mean level_std levels_mean levels_std rooms_mean rooms_std area_mean area_std kitchen_area_mean kitchen_area_std
0 0 5.284833e+06 1.279574e+07 53.747444 5.098192 55.518322 22.517204 8.851292 5.923950 16.738440 6.499030 1.686900 0.934588 58.559349 37.077482 12.792236 6.599672
1 1 3.512978e+06 1.952254e+07 54.790298 3.357936 61.443077 21.923780 5.571202 4.060143 10.213742 4.642660 1.794023 0.966615 51.407722 25.746609 9.298869 4.062081
2 2 7.126816e+06 1.267507e+07 53.999779 5.346035 42.106968 13.565219 9.031732 6.130808 16.850670 6.862993 1.697287 0.928301 57.157982 32.994183 13.154365 6.146328
3 3 4.068649e+06 8.331654e+06 53.556213 4.910420 51.249592 19.097178 4.929223 3.997643 8.741839 5.626125 1.878324 0.944347 55.062199 36.979730 10.442657 6.912905
4 4 4.004994e+06 3.367351e+07 51.181896 5.909806 47.777088 17.570242 5.430987 4.308443 9.746289 5.743188 1.811687 0.892482 52.034588 42.411056 10.051356 4.793207
5 5 2.618608e+06 1.543122e+07 56.440563 3.981515 63.951946 28.464556 2.472882 3.850120 3.766808 5.931042 2.031180 0.907434 50.446354 52.361071 9.285330 4.603177

lineplot


sns.lineplot(aggregated_df,x='building_type',y='price_mean', label='mean')
<Axes: xlabel='building_type', ylabel='price_mean'>

subplots

fig, axs = plt.pyplot.subplots(2,2)
fig.tight_layout(pad=1)
fig.set_size_inches(16.5, 14, forward=True)

sns.lineplot(aggregated_df,x='building_type',y='price_mean', label='mean', ax=axs[0,0])

sns.lineplot(aggregated_df,x='building_type',y='area_mean', label='area',ax=axs[0,1])
sns.lineplot(aggregated_df,x='building_type',y='kitchen_area_mean', label='kitchen_area_mean',ax=axs[0,1])

sns.lineplot(aggregated_df,x='building_type',y='level_mean', label='level_mean',ax=axs[1,0])

# Группировка непосредственно в seaborn
sns.lineplot(df, x='building_type', y='area', label='area', ax=axs[1,1])
<Axes: xlabel='building_type', ylabel='area'>

Bokeh

https://bokeh.org/

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource,  HoverTool, Legend
from bokeh.io import output_notebook 
output_notebook()
Loading BokehJS ... 
""
source = ColumnDataSource(data=df.sample(frac=0.1))
p = figure(width=1000)
p.scatter(source=source, x='area', y='price' )
hover = HoverTool(tooltips=[('flat area ', '@area'),
                              ('price', '@price'),
                              ('level', '@level')])

p.add_tools(hover)
show(p)
from bokeh.transform import factor_cmap

types = df['floor_level'].unique().tolist()
source = ColumnDataSource(data=df.sample(frac=0.1))
p = figure(width=1000)
p.scatter(source=source, x='area', y='level', color=factor_cmap('floor_level', 'Category10_5', types) )
hover = HoverTool(tooltips=[('area', '@area'),
                              ('price', '@price'),
                              ('level', '@level')])

p.add_tools(hover)

show(p)

Save clean dataset

# Сохраняем обработанный датафрейм, чтобы на следующих этапах не проводить повторно ту же обработку.
# Лучше сохранить в pickle формате, чтобы сохранились все типы данных, в т.ч. category
df.to_pickle('./data/clean_data.pkl')
# Считать можно так:
df = pd.read_pickle('./data/clean_data.pkl')
df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 5393546 entries, 0 to 5477005
Data columns (total 11 columns):
 #   Column         Dtype   
---  ------         -----   
 0   price          int64   
 1   geo_lat        float32 
 2   geo_lon        float32 
 3   region         category
 4   building_type  category
 5   level          int8    
 6   levels         int8    
 7   rooms          int8    
 8   area           float16 
 9   kitchen_area   float16 
 10  object_type    category
dtypes: category(3), float16(2), float32(2), int64(1), int8(3)
memory usage: 174.9 MB

Выводы после EDA

Выводы, полученные в ходе анализа:

  • перечислить все действия, проведенные на этапе очистки данных
  • Создавались ли новые признаки?
  • Какие закономерности выявлены по графикам, которые могут быть полезны в дальнейшем для решния задачи?