Titanic tragedy: finding and analyzing the survivor rate. titanic Live. Visualizing Data. Name, Sex, Age: 이름 그대로 의미입니다. So, it is very important to remove null values from the dataset before applying any machine learning algorithm to that dataset. Sex: Sex of the passenger. titanic dataset Now I have downloaded the said csv file and saved it as 'scatter_plot_data.csv' and have used the following code to create the scatter plot in matplotlib using python and pandas. You can convert them to a pandas DataFrame using the read_csv function. Pandas read_csv() – How to read a csv file in Python On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. I wanted to try out some of what I’ve learned with python for data science. Become a Patron! The data. titanic. import numpy as np import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv ('train.csv') df = df [ ['Age', 'Sex', 'Pclass', 'Survived']] print df.describe () print df.info () Age Pclass Survived count 714.000000 891.000000 891.000000 mean 29.699118 2.308642 0.383838 std 14.526497 0.836071 0.486592 min 0.420000 1.000000 0.000000 Titanic Tragedy: Exploratory Data Analysis Modeling Data: To model the dataset, we apply logistic regression. Inspired from R DataFrame and Python pandas, Spark DataFrame is the newer data format supported by Spark.
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