Data Frame 2 Series 1 3 Data Frame
- Slides: 75
概要 • Data. Frame ( 2次 ) と Series ( 1次 ) 3
概要 • Data. Frame ( 2次 ) と Series ( 1次 ) 4
概要 • Data. Frame ( 2次 ) と Series ( 1次 ) 5
Series, Data. Frame の作成 • Data. Frame df = pd. Data. Frame( [( 'Alice', 20), ( 'Bob', 24)] , columns=[ 'Name' , 'Age' ]) もしくは df = pd. Data. Frame( { 'Name' : [ 'Alice', 'Bob' ] , 'Age' : [20 , 24] } ) 8
Series, Data. Frame の作成 • Data. Frame df = pd. Data. Frame( [( 'Alice', 20), ( 'Bob', 24)] , columns=[ 'Name' , 'Age' ]) もしくは df = pd. Data. Frame( { 'Name' : [ 'Alice', 'Bob' ] , 'Age' : [20 , 24] } ) 9
Series, Data. Frame の作成 • Data. Frame df = pd. Data. Frame( [( 'Alice', 20), ( 'Bob', 24)] , columns=[ 'Name' , 'Age' ]) もしくは df = pd. Data. Frame( { 'Name' : [ 'Alice', 'Bob' ] , 'Age' : [20 , 24] } ) 10
データの参照 • 行や列だけを確認したいとき – 行: df. index Index(['First', 'Second', 'Third', 'Fourth'], dtype='object') – 列: df. columns Index(['dice 1', 'dice 2', 'dice 3'], dtype='object') 22
iris データを用いた計算や描画 pd. melt(df, id_vars = ['species'], id_name = 'target', value_vars = ['sepal_length'], value_name = 'val') 57
iris データを用いた計算や描画 – df. groupby( [ 'species', ' target ' ] ). mean(). reset_index() • アヤメの種類ごとのがくの高さなどの平均を表示する – df. groupby( [ 'species', ' target ' ] ). max(). reset_index() – df. groupby( [ 'species', ' target ' ] ). min(). reset_index() 58
iris データを用いた計算や描画 – df. groupby( [ 'species', ' target ' ] ). mean(). reset_index() • アヤメの種類ごとのがくの高さなどの平均を表示する – df. groupby( [ 'species', ' target ' ] ). max(). reset_index() – df. groupby( [ 'species', ' target ' ] ). min(). reset_index() – df. pivot( index = 'target', columns = 'species', values = 'val' ) 59
iris データを用いた計算や描画 • 描画 – matplotlib を用いての 描画が簡単に行える – import matplotlib. pyplot as plt – % matplotlib inline 61
iris データを用いた計算や描画 • 散布図 – df. plot('sepal_length', 'sepal_width', kind='scatter') • ヒストグラム – df. hist() 63
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