pandas: powerful Python data analysis toolkit - 0.7.1X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null values ibb 100 non-null values hbp 100 object g int64 ab int64 r int64 h int64 X2b int64 X3b int64 hr int64 rbi float64 sb float64 cs float64 bb int64 so float64 ibb float64 hbp float64 sh float64 sf float64 gidp float64 The0 码力 | 281 页 | 1.45 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.7.2X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null values ibb 100 non-null values hbp 100 object g int64 ab int64 r int64 h int64 X2b int64 X3b int64 hr int64 rbi float64 sb float64 cs float64 bb int64 so float64 ibb float64 hbp float64 sh float64 sf float64 gidp float64 The0 码力 | 283 页 | 1.45 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.7.3X3b 100 non-null values hr 100 non-null values rbi 100 non-null values sb 100 non-null values cs 100 non-null values bb 100 non-null values so 100 non-null values ibb 100 non-null values hbp 100 object g int64 ab int64 r int64 h int64 X2b int64 X3b int64 hr int64 rbi float64 sb float64 cs float64 bb int64 so float64 ibb float64 hbp float64 sh float64 sf float64 gidp float64 The0 码力 | 297 页 | 1.92 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25read_csv('data/baseball.csv') In [120]: print(baseball) id player year stint team lg g ab r h ... rbi sb �→ cs bb so ibb hbp sh sf gidp 0 88641 womacto01 2006 2 CHN NL 19 50 6 14 ... 2.0 1.0 �→1.0 4 4.0 0.0 0 int64 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 3.4. Intro 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb so �→ibb hbp sh sf gidp year team �→ 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 1270 码力 | 698 页 | 4.91 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 1.0.0read_csv('data/baseball.csv') In [118]: print(baseball) id player year stint team lg g ab r h X2b ... hr rbi sb ˓→ cs bb so ibb hbp sh sf gidp 0 88641 womacto01 2006 2 CHN NL 19 50 6 14 1 ... 1 2.0 1.0 ˓→ 1.0 4 4.0 non-null int64 12 hr 100 non-null int64 13 rbi 100 non-null float64 14 sb 100 non-null float64 15 cs 100 non-null float64 (continues on next page) 162 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.0 (continued from previous page) stint g ab r h X2b X3b hr rbi sb cs bb so ˓→ibb hbp sh sf gidp year team ˓→ 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 1270 码力 | 3015 页 | 10.78 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.0read_csv('data/baseball.csv') In [120]: print(baseball) id player year stint team lg g ab r h X2b X3b hr rbi sb ˓→ cs bb so ibb hbp sh sf gidp 0 88641 womacto01 2006 2 CHN NL 19 50 6 14 1 0 1 2.0 1.0 ˓→ 1.0 4 4.0 0 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 hbp 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb so ˓→ibb hbp sh sf gidp year team ˓→ 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 1270 码力 | 2827 页 | 9.62 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.25.1read_csv('data/baseball.csv') In [120]: print(baseball) id player year stint team lg g ab r h X2b X3b hr rbi sb ˓→ cs bb so ibb hbp sh sf gidp 0 88641 womacto01 2006 2 CHN NL 19 50 6 14 1 0 1 2.0 1.0 ˓→ 1.0 4 4.0 0 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 hbp 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb so ˓→ibb hbp sh sf gidp year team ˓→ 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 1270 码力 | 2833 页 | 9.65 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.19.0sum() ....: .loc[lambda df: df.r > 100] ....: ) ....: Out[21]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 4 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 hbp 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 40 码力 | 1937 页 | 12.03 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.19.1sum() ....: .loc[lambda df: df.r > 100] ....: ) ....: Out[21]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 4 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 hbp 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 40 码力 | 1943 页 | 12.06 MB | 1 年前3
pandas: powerful Python data analysis toolkit - 0.20.3sum() ....: .loc[lambda df: df.r > 100] ....: ) ....: Out[21]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 4 X3b 100 non-null int64 hr 100 non-null int64 rbi 100 non-null float64 sb 100 non-null float64 cs 100 non-null float64 bb 100 non-null int64 so 100 non-null float64 ibb 100 non-null float64 hbp 'team']).sum() ....: .loc[lambda df: df.r > 100]) ....: Out[92]: stint g ab r h X2b X3b hr rbi sb cs bb \ year team 2007 CIN 6 379 745 101 203 35 2 36 125.0 10.0 1.0 105 DET 5 301 1062 162 283 54 40 码力 | 2045 页 | 9.18 MB | 1 年前3
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