I tried Python! ] Titanic data _vol.1 (bar graph, scatter plot, correlation coefficient)

Today, without thinking too difficult For the time being, using "Titanic data", We will do a statistical analysis.

• Today is for super beginners! (Bar graph, scatter plot, correlation coefficient up to about)

■ Introduction

My execution environment

Microsoft Windows Version：10.0 Python Version：3.8.1

• Use command prompt (I've heard rumors that Linux is better, but I have a windows PC ... So for the time being, I'm doing my best on windows. )

↓ (Reference) Check the version of windows

C:\Users\User name>ver

↓ (Reference) Python version check

C:\Users\User name>python

About virtual environment

Python seems to be convenient to run in a virtual environment, so I also use a virtual environment.

• I would like to introduce how to create a virtual environment at a later date.

↓ (Reference) Launching a virtual environment

C:\Users\User name>Virtual environment name\scripts\activate

↓ (Reference) When the virtual environment is started, it will be displayed like this

(Virtual environment name)C:\Users\User name>

■ Installation of required packages

The package used this time is ・ Numpy ・ Pandas ・ Matplotlib ・ Seaborn is.

↓ Installation

(Virtual environment name)C:\Users\User name>pip install package name

↓ List of installed packages

(Virtual environment name)C:\Users\User name>pip list

↓ The result looks like this.

■ Start python

(Virtual environment name)C:\Users\User name>python

↓ (Reference) When Python starts, it will be displayed like this (Only >>> is displayed ...)

>>>

■ Reading data

This time, we will use "Titanic Data" available from the global data competition "Kaggle".

• I hope I can introduce the details later.

Data storage folder

I myself am a super beginner and I didn't understand well, so I once saved the data directly under the "C: \ Users \ username" folder. (I tried absolute paths and relative paths, but for some reason it didn't work ... (TT))

Data reading

↓ Package import

import pandas as pd

• When completed, ">>>" will appear again.

↓ Use the "read_csv" class of "pandas (pd)" to "train.csv" Store in "df".

df = pd.read_csv("train.csv")

■ Finally try data analysis!

Once you've come this far, you can look at the data as you like.

Number of survivors and deaths

After importing the required packages Let's display the data item "Survived (survivor = 1, death = 0)" stored in "df" on a bar graph.

import seaborn as sns
import matplotlib.pyplot as plt
sns.countplot("Survived",data=df,palette='rainbow')
plt.show()

↓ "plt.show ()" execution result It was convenient to be able to adjust the vertical and horizontal deviations and save images from here!

Correlation coefficient matrix (heat map)

sns.heatmap(df.corr(),annot=True,cmap='RdYlGn',vmin=-1,vmax=1,fmt=".2f",square=True)
plt.show()

Pair plot diagram

sns.pairplot(df)
plt.show()

■ Finally

Thank you for reading today. This post was the first post, and I think there were some things that were difficult to understand. Please pardon. If you have any suggestions, we will accept them (I don't know how to accept them ...) Thank you very much. If I find an error myself, I will correct it each time. I will answer as many questions as possible (I don't know how to accept this either ...) Please feel free to ask any questions. See you somewhere again ~ (^^) ♪