Pythonでの金融データのインポートと管理
Stefan Jansen
Instructor
pd.DataFrame() が CSV ソース {{1}} と同一であることを確認amex-listings.csv
dtype)がありますdtype は計算や可視化に影響しますpandas dtype |
列の特徴 |
|---|---|
object |
文字列、または文字列と数値の混在 |
int64 |
数値: 整数(64 ビット、$\le 2^{64}$) |
float64 |
数値: 小数、または欠損を含む整数 |
datetime64 |
日時情報 |
import pandas as pdamex = pd.read_csv('amex-listings.csv')amex.info() # 表構造とデータ型を確認
RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 Stock Symbol 360 non-null object
1 Company Name 360 non-null object
2 Last Sale 346 non-null float64
3 Market Capitalization 360 non-null float64
4 IPO Year 105 non-null float64
5 Sector 238 non-null object
6 Industry 238 non-null object
7 Last Update 360 non-null object
dtypes: float64(3), object(5)
# 'n/a' を np.nan に置換 amex = pd.read_csv('amex-listings.csv', na_values='n/a')amex.info()
RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 Stock Symbol 360 non-null object
1 Company Name 360 non-null object
2 Last Sale 346 non-null float64
3 Market Capitalization 360 non-null float64
4 IPO Year 105 non-null float64
5 Sector 238 non-null object
6 Industry 238 non-null object
7 Last Update 360 non-null object
dtypes: float64(3), object(5)
amex = pd.read_csv('amex-listings.csv', na_values='n/a', parse_dates=['Last Update'])amex.info()
RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 Stock Symbol 360 non-null object
1 Company Name 360 non-null object
2 Last Sale 346 non-null float64
3 Market Capitalization 360 non-null float64
4 IPO Year 105 non-null float64
5 Sector 238 non-null object
6 Industry 238 non-null object
7 Last Update 360 non-null datetime64[ns]
dtypes: datetime64[ns](1), float64(3), object(4)
amex.head(2) # 先頭 n 行を表示(既定: 5)
Stock Symbol Company Name
0 XXII 22nd Century Group, Inc
1 FAX Aberdeen Asia-Pacific Income Fund Inc
Last Sale Market Capitalization IPO Year
0 1.3300 1.206285e+08 NaN
1 5.0000 1.266333e+09 1986.0
Sector Industry Last Update
0 Non-Durables Farming/Seeds/Milling 2017-04-26
1 NaN NaN 2017-04-25
Pythonでの金融データのインポートと管理