Basic visualization techniques learned from Kaggle Titanic data
Introduction
[Updated from time to time] Mainly using the snippets in EDA / Feature Engineering Snippets Used in Kaggle Table Data Competition [Kaggle Titanic Data] Use (https://www.kaggle.com/c/titanic/data) to visualize basic data.
Premise
import numpy as np
import pandas as pd
import pandas_profiling as pdp
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
cmap = plt.get_cmap("tab10")
plt.style.use('fivethirtyeight')
%matplotlib inline
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option("display.max_colwidth", 10000)
target_col = "Survived"
data_dir = "/kaggle/input/titanic/"
Check the folder
!ls -GFlash /kaggle/input/titanic/
total 100K
4.0K drwxr-xr-x 2 nobody 4.0K Jan 7 2020 ./
4.0K drwxr-xr-x 5 root 4.0K Jul 12 00:15 ../
4.0K -rw-r--r-- 1 nobody 3.2K Jan 7 2020 gender_submission.csv
28K -rw-r--r-- 1 nobody 28K Jan 7 2020 test.csv
60K -rw-r--r-- 1 nobody 60K Jan 7 2020 train.csv
Read data
train = pd.read_csv(data_dir + "train.csv")
test = pd.read_csv(data_dir + "test.csv")
submit = pd.read_csv(data_dir + "gender_submission.csv")
Check the data
train.head()
Check the number of records and columns
print("{} rows and {} features in train set".format(train.shape[0], train.shape[1]))
print("{} rows and {} features in test set".format(test.shape[0], test.shape[1]))
print("{} rows and {} features in submit set".format(submit.shape[0], submit.shape[1]))
891 rows and 12 features in train set
418 rows and 11 features in test set
418 rows and 2 features in submit set
Check the number of defects for each column
Check how many defects are in each column.
train.isnull().sum()
PassengerId 0
Survived 0
Pclass 0
Name 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
Fare 0
Cabin 687
Embarked 2
dtype: int64
Visualization of missing values
Check if the defect has regularity.
plt.figure(figsize=(18,9))
sns.heatmap(train.isnull(), cbar=False)
Check the summary statistics for each column
Check the summary statistics such as mean, standard deviation, maximum, minimum, and mode for each column to get a rough idea of the data.
train.describe()
Aggregate the number (frequency) of data
Check the target percentage
sns.countplot(x=target_col, data=train)
Check the percentage of category values
col = "Pclass"
sns.countplot(x=col, data=train)
Check the percentage of a column for each target value
col = "Pclass"
sns.countplot(x=col, hue=target_col, data=train)
col = "Sex"
sns.countplot(x=col, hue=target_col, data=train)
histogram
The vertical axis is frequency and the horizontal axis is class, which visualizes the distribution of data. Try some to show different data characteristics for different bin sizes.
col = "Age"
train[col].plot(kind="hist", bins=10, title='Distribution of {}'.format(col))
col = "Fare"
train[col].plot(kind="hist", bins=50, title='Distribution of {}'.format(col))
Histogram by category
f, ax = plt.subplots(1, 3, figsize=(15, 4))
sns.distplot(train[train['Pclass']==1]["Fare"], ax=ax[0])
ax[0].set_title('Fares in Pclass 1')
sns.distplot(train[train['Pclass']==2]["Fare"], ax=ax[1])
ax[1].set_title('Fares in Pclass 2')
sns.distplot(train[train['Pclass']==3]["Fare"], ax=ax[2])
ax[2].set_title('Fares in Pclass 3')
plt.show()
Histogram of columns by target category
col = "Age"
fig, ax = plt.subplots(1, 2, figsize=(15, 6))
train[train[target_col]==1][col].plot(kind="hist", bins=50, title='{} - {} 1'.format(col, target_col), color=cmap(0), ax=ax[0])
train[train[target_col]==0][col].plot(kind="hist", bins=50, title='{} - {} 0'.format(col, target_col), color=cmap(1), ax=ax[1])
plt.show()
Histogram of a column for each target value (when overlapping)
col = "Age"
train[train[target_col]==1][col].plot(kind="hist", bins=50, alpha=0.3, color=cmap(0))
train[train[target_col]==0][col].plot(kind="hist", bins=50, alpha=0.3, color=cmap(1))
plt.title("histgram for {}".format(col))
plt.xlabel(col)
plt.show()
Kernel density estimation
Roughly speaking, it is a curved histogram. You can get Y for X.
sns.kdeplot(label="Age", data=train["Age"], shade=True)
Cross tabulation
Calculate the number of occurrences of each category of category data.
pd.crosstab(train["Sex"], train["Pclass"])
pd.crosstab([train["Sex"], train["Survived"]], train["Pclass"])
Pivot table
Average of quantitative data by category
pd.pivot_table(index="Pclass", columns="Sex", data=train[["Age", "Fare", "Survived", "Pclass", "Sex"]])
Minimum value of quantitative data for each category
pd.pivot_table(index="Pclass", columns="Sex", data=train[["Age", "Fare", "Pclass", "Sex"]], aggfunc=np.min)
Scatter plot
Check the relationship between the two columns.
Scatter plot
sns.scatterplot(x="Age", y="Fare", data=train)
Scatter plot (color-coded by category)
sns.scatterplot(x="Age", y="Fare", hue=target_col, data=train)
Scatterplot matrix
sns.pairplot(data=train[["Fare", "Survived", "Age", "Pclass"]], hue="Survived", dropna=True)
Box plot
Visualize data variability.
Box plot by category
Check the variation of data for each category.
sns.boxplot(x='Pclass', y='Age', data=train)
Strip chart
The figure which represented the data by a dot. It is used when one of the two data is categorical.
sns.stripplot(x="Survived", y="Fare", data=train)
sns.stripplot(x='Pclass', y='Age', data=train)
Heat map
Heat map of correlation coefficient for each column
sns.heatmap(train.corr(), annot=True)
reference
- EDA To Prediction(DieTanic)
- A Simple Tutorial To Data Visualization -[Python: Try visualization using seaborn](https://blog.amedama.jp/entry/seaborn-plot#scatter-plot-%E6%95%A3%E5%B8%83%E5% 9B% B3) -Calculate statistics for each category with pandas pivot table
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