# Pythonでの金融データのインポートと管理
This is a DataCamp course: このコースでは、Pythonで多様なツールやデータ源を用いて金融データを取り込み、管理する方法を学びます。
## Course Details
- **Duration:** ~5h
- **Level:** Intermediate
- **Instructor:** Stefan Jansen
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Applied Finance, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Data Manipulation with pandas
## Learning Outcomes
- Python
- Applied Finance
- Data Science and Analytics
- Pythonでの金融データのインポートと管理
## Traditional Course Outline
1. Importing stock listing data from Excel - In this chapter, you will learn how to import, clean and combine data from Excel workbook sheets into a pandas DataFrame. You will also practice grouping data, summarizing information for categories, and visualizing the result using subplots and heatmaps. You will use data on companies listed on the stock exchanges NASDAQ, NYSE, and AMEX with information on company name, stock symbol, last market capitalization and price, sector or industry group, and IPO year. In Chapter 2, you will build on this data to download and analyze stock price history for some of these companies.
2. Importing financial data from the web - This chapter introduces online data access to Google Finance and the Federal Reserve Data Service through the `pandas` `DataReader`. You will pull data, perform basic manipulations, combine data series, and visualize the results.
3. Summarizing your data and visualizing the result - In this chapter, you will learn how to capture key characteristics of individual variables in simple metrics. As a result, it will be easier to understand the distribution of the variables in your data set: Which values are central to, or typical of your data? Is your data widely dispersed, or rather narrowly distributed around some mid point? Are there outliers? What does the overall distribution look like?
4. Aggregating and describing your data by category - This chapter introduces the ability to group data by one or more categorical variables, and to calculate and visualize summary statistics for each caategory. In the process, you will learn to compare company statistics for different sectors and IPO vintages, analyze the global income distribution over time, and learn how to create various statistical charts from the seaborn library.
## Resources and Related Learning
**Resources:** Amex listings .csv file (dataset), Income growth .csv file (dataset), Listings .xlsx file (dataset), Nasdaq listings .csv file (dataset), Per capita income .csv file (dataset)
**Related tracks:** ファイナンスの基礎 Pythonで
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コース
Pythonでの金融データのインポートと管理
中級スキルレベル
更新日 2023/02PythonApplied Finance5時間16 ビデオ53 演習4,350 XP44,859達成証明書
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前提条件
Data Manipulation with pandas1
Importing stock listing data from Excel
In this chapter, you will learn how to import, clean and combine data from Excel workbook sheets into a pandas DataFrame. You will also practice grouping data, summarizing information for categories, and visualizing the result using subplots and heatmaps.
You will use data on companies listed on the stock exchanges NASDAQ, NYSE, and AMEX with information on company name, stock symbol, last market capitalization and price, sector or industry group, and IPO year. In Chapter 2, you will build on this data to download and analyze stock price history for some of these companies.
2
Importing financial data from the web
This chapter introduces online data access to Google Finance and the Federal Reserve Data Service through the
pandas DataReader. You will pull data, perform basic manipulations, combine data series, and visualize the results.3
Summarizing your data and visualizing the result
In this chapter, you will learn how to capture key characteristics of individual variables in simple metrics. As a result, it will be easier to understand the distribution of the variables in your data set: Which values are central to, or typical of your data? Is your data widely dispersed, or rather narrowly distributed around some mid point? Are there outliers? What does the overall distribution look like?
4
Aggregating and describing your data by category
This chapter introduces the ability to group data by one or more categorical variables, and to calculate and visualize summary statistics for each caategory. In the process, you will learn to compare company statistics for different sectors and IPO vintages, analyze the global income distribution over time, and learn how to create various statistical charts from the seaborn library.
Pythonでの金融データのインポートと管理
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