Costa Rican Household Poverty Level Prediction

Imports & Read Data

# Data manipulation
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Set a few plotting defaults
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['font.size'] = 18
plt.rcParams['patch.edgecolor'] = 'k'

• 데이터 읽어서 정보 요약

pd.options.display.max_columns = 150

# Read in data
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head()

train.info()

• Integer Columns

train.select_dtypes(np.int64).nunique().value_counts().sort_index().plot.bar(color = 'blue', 
                                                                             figsize = (8, 6),
                                                                            edgecolor = 'k', linewidth = 2);
plt.xlabel('Number of Unique Values'); plt.ylabel('Count');
plt.title('Count of Unique Values in Integer Columns');