Indexing and selecting data — pandas 2.2.3 documentation
Indexing and selecting data# The axis labeling information in pandas objects serves many purposes: Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display. Enables automatic and explicit data alignment. Allows intuitive getting and setting of subsets of the data set. In this section, we will focus on the final point: n
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pandas.crosstab()関数を使ã†ã¨ã‚¯ãƒã‚¹é›†è¨ˆåˆ†æžãŒã§ãる。 カテゴリデータ（カテゴリカルデータã€è³ªçš„データ）ã®ã‚«ãƒ†ã‚´ãƒªã”ã¨ã®ã‚µãƒ³ãƒ—ル数（出ç¾å›žæ•°ãƒ»é »åº¦ï¼‰ã®ç®—出ãªã©ãŒå¯èƒ½ã€‚ pandas.crosstab — pandas 0.22.0 documentation 出ç¾å›žæ•°ã§ã¯ãªãã€ã‚«ãƒ†ã‚´ãƒªã”ã¨ã®å¹³å‡å€¤ãªã©ã‚’算出ã—ãŸã„å ´åˆã¯ãƒ”ボットテーブルpandas.pivot_table()を使ã†ã€‚以下ã®è¨˜äº‹ã‚’å‚照。 関連記事: pandasã®ãƒ”ボットテーブルã§ã‚«ãƒ†ã‚´ãƒªæ¯Žã®çµ±è¨ˆé‡ãªã©ã‚’算出 ã“ã“ã§ã¯ã€ pandas.crosstab()関数ã®åŸºæœ¬çš„ãªä½¿ã„æ–¹ カテゴリã”ã¨ã®å°è¨ˆãƒ»ç·è¨ˆã‚’算出: 引数margins 全体・行ã”ã¨ãƒ»åˆ—ã”ã¨ã«è¦æ ¼åŒ–（æ£è¦åŒ–）: 引数normalize ã«ã¤ã„ã¦èª¬æ˜Žã™ã‚‹ã€‚ 例ã¨ã—ã¦ã‚¿ã‚¤ã‚¿ãƒ‹ãƒƒã‚¯ã®ç”Ÿå˜æƒ…å ±ã®ãƒ‡ãƒ¼ã‚¿ã‚’使用ã™ã‚‹ã€‚Kaggleã®å•é¡Œã‹ã‚‰ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã§ãる。 im
Is there a parameter in matplotlib/pandas to have the Y axis of a histogram as percentage?
I would like to compare two histograms by having the Y axis show the percentage of each column from the overall dataset size instead of an absolute value. Is that possible? I am using Pandas and matplotlib. Thanks
Python pandas パフォーマンスç¶æŒã®ãŸã‚ã® 3 ã¤ã® TIPS - StatsFragments
pandas ã§ãã“ãã“大ãã„データを扱ã†å ´åˆã€ãã®å‡¦ç†é€Ÿåº¦ãŒæ°—ã«ãªã£ã¦ãる。公å¼ãƒ‰ã‚ュメントã§ã¯ãƒ‘フォーマンスå‘上ã®ãŸã‚ã« Cython ã‚„ Numba を使ã†æ–¹æ³•ã‚’記載ã—ã¦ã„る。 Enhancing Performance — pandas 0.16.2 documentation ãŒã€è»½ã試ã—ãŸã„ã ã‘ãªã®ã« ã‚ã–ã‚ã– Cythonã‚„ Numba を使ã†ã®ã¯æ‰‹é–“ã ã—ã€ã‹ã¨ã„ã£ã¦ã‚ã¾ã‚Šã«é…ã„ã®ã‚‚å«Œã 。ãã‚“ãªã¨ãã€pandas 本æ¥ã®ãƒ‘フォーマンスをã§ãã‚‹ã ã‘ç¶æŒã™ã‚‹ãŸã‚ã®ãƒã‚¤ãƒ³ãƒˆã‚’æ•´ç†ã—ãŸã„。 pandas ã«é™ã‚‰ãšã€ãƒ‘フォーマンス改善ã®éš›ã«ã¯ãƒœãƒˆãƒ«ãƒãƒƒã‚¯ã®ç®‡æ‰€ã«ã‚ˆã£ã¦ã¨ã‚‹ã¹ã対ç–ã¯ç•°ãªã‚‹ã€‚pandas ã§ã¯é€Ÿåº¦å‘上/エッジケース処ç†ã®ãŸã‚㫠データã®åž‹ã‚„æ¡ä»¶ã«ã‚ˆã£ã¦å†…部ã§å‡¦ç†ã‚’ç´°ã‹ã分ã‘ã¦ãŠã‚Šã€å¸¸ã«ã“ã†ã™ã‚Œã°é€Ÿããªã‚‹ï¼ ã¨ã„ã†æ–¹æ³•ã‚’出ã™ã®ã¯é›£ã—ã„。以下ã¯ã“ã®å‰æã®ã†ãˆã§ã€å†…部実装ã‹ã‚‰ã¿
pandasã§CSV/TSVファイルèªã¿è¾¼ã¿ï¼ˆread_csv, read_table） | note.nkmk.me
pandasã§CSVファイルやTSVファイルをDataFrameã¨ã—ã¦èªã¿è¾¼ã‚€ã«ã¯read_csv()を使ã†ã€‚ pandas.read_csv — pandas 2.0.3 documentation IO tools (text, CSV, HDF5, …) - CSV & text files — pandas 2.0.3 documentation CSVファイルã¸ã®æ›¸ãè¾¼ã¿ãƒ»è¿½è¨˜ã«ã¤ã„ã¦ã¯ä»¥ä¸‹ã®è¨˜äº‹ã‚’å‚照。 関連記事: pandasã§CSVファイルã®æ›¸ãè¾¼ã¿ãƒ»è¿½è¨˜ï¼ˆto_csv） pandasã§ã®Excel, JSON, pickleファイルã®èªã¿æ›¸ã（入出力）ã«ã¤ã„ã¦ã¯ä»¥ä¸‹ã®è¨˜äº‹ã‚’å‚照。 関連記事: pandasã§Excelファイル（xlsx, xls）ã®èªã¿è¾¼ã¿ï¼ˆread_excel） 関連記事: pandasã§Excelファイル（xlsx, xls）ã®æ›¸ãè¾¼ã¿ï¼ˆto_exce
[Python]Pandasã®DataFrameã«ãŠã‘る逆数ã®å–ã‚Šæ–¹ | maluli
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How can get ' USDJPY'(currency rates) with pandas and yahoo finance?
I am learning and using the pandas and python. Today, I am trying to make a fx rate table, but I got a trouble with getting the pricess of 'USDJPY'. When I get a prices of 'EUR/USD', i code like this. eur = web.DataReader('EURUSD=X','yahoo')['Adj Close'] it works. But when I wrote jpy = web.DataReader('USDJPY=X','yahoo')['Adj Close'] the error message comes like this: -----------------------------
Intro to Data Structures — pandas 0.23.0 documentation
Table Of Contents What’s New Installation Contributing to pandas Package overview 10 Minutes to pandas Tutorials Cookbook Intro to Data Structures Series Series is ndarray-like Series is dict-like Vectorized operations and label alignment with Series Name attribute DataFrame From dict of Series or dicts From dict of ndarrays / lists From structured or record array From a list of dicts From a dict
<Python, pandas> è¡Œ(row)ã‚’è¿½åŠ ã™ã‚‹ã€‚ - ãã“ゆãã®ãƒ¡ãƒ¢
è¡Œrowã‚’è¿½åŠ ã™ã‚‹ã€‚è¡Œã®è¿½åŠ 。 ã“ã†ã„ã†ãƒ‡ãƒ¼ã‚¿ãƒ•ãƒ¬ãƒ¼ãƒ DataFrameãŒã‚ã£ãŸæ™‚〠In [71]: df Out[71]: id name birthday is_relative 0 1 Hage 1960-01-01 1 1 2 Hige 1960-01-02 1 ï¼‘è¡Œè¿½åŠ ã—ãŸã„時ã¯ã€ã‚·ãƒªãƒ¼ã‚ºSeriesã§ãƒ‡ãƒ¼ã‚¿ä½œã£ã¦ã€.append()ã™ã‚Œã°ã„ã„ã£ã½ã„。 ã‚„ã£ã¦ã¿ãŸã€‚ In [74]: se = pd.Series([3, 'Hoge', date(1960,1,3), 1], index=df.columns) In [75]: se Out[75]: id 3 name Hoge birthday 1960-01-03 is_relative 1 dtype: object In [76]: df.append(se, ignore_index=True) Out[76]:
pandas.DataFrameã®forループ処ç†ï¼ˆã‚¤ãƒ†ãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³ï¼‰ | note.nkmk.me
Pythonã«ãŠã‘ã‚‹foræ–‡ã«ã¤ã„ã¦ã®è©³ç´°ã¯ä»¥ä¸‹ã®è¨˜äº‹ã‚’å‚照。 関連記事: Pythonã®foræ–‡ã«ã‚ˆã‚‹ãƒ«ãƒ¼ãƒ—処ç†ï¼ˆrange, enumerate, zipãªã©ï¼‰ 本記事ã®ã‚µãƒ³ãƒ—ルコードã®pandasã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯ä»¥ä¸‹ã®é€šã‚Šã€‚ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã«ã‚ˆã£ã¦ä»•æ§˜ãŒç•°ãªã‚‹å¯èƒ½æ€§ãŒã‚ã‚‹ã®ã§æ³¨æ„。以下ã®DataFrameを例ã¨ã™ã‚‹ã€‚ import pandas as pd print(pd.__version__) # 2.1.4 df = pd.DataFrame({'age': [24, 42], 'state': ['NY', 'CA'], 'point': [64, 92]}, index=['Alice', 'Bob']) print(df) # age state point # Alice 24 NY 64 # Bob 42 CA 92
pandasã®ç´°ã‹ã„メモ - ã—ãŒãªã„エンジニアã®ãƒ–ãƒã‚°
pandasã‚„ã£ã¦ã¦åœ°å‘³ã ãŒä¾¿åˆ©ãªæ“作を使ã†ã“ã¨ãŒã‚ã£ãŸã®ã§ãƒ¡ãƒ¢ Series.to_frame()ã§ä¸€æ°—ã«DataFrame化 Seriesã«ã¯to_frame()ã¨ã„ã†é–¢æ•°ãŒã‚ã‚Šã€ã™ãã«DataFrame化ã™ã‚‹ã“ã¨ãŒã§ãã‚‹ ã“ã‚Œã§ä»–ã®DataFrameã¨mergeã—ãŸã‚Šã™ã‚‹ã“ã¨ãŒã§ãã‚‹ df = pd.Series(pd.date_range(start='2016-01-01', end='2016-12-31', freq='D', name="Time")).to_frame() print(type(df)) print(df.head()) print(type(df.iloc[0, 0])) print(df.iloc[0, 0]) çµæžœ <class 'pandas.core.frame.DataFrame'> Time 0 2016-01-01 1 2016-01-02
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