{"id":968,"date":"2023-07-28T03:42:21","date_gmt":"2023-07-28T03:42:21","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%afcsv%e3%82%92%e8%aa%ad%e3%81%bf%e5%8f%96%e3%82%8a%e3%81%be%e3%81%99\/"},"modified":"2023-07-28T03:42:21","modified_gmt":"2023-07-28T03:42:21","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%afcsv%e3%82%92%e8%aa%ad%e3%81%bf%e5%8f%96%e3%82%8a%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%afcsv%e3%82%92%e8%aa%ad%e3%81%bf%e5%8f%96%e3%82%8a%e3%81%be%e3%81%99\/","title":{"rendered":"\u7a76\u6975\u30ac\u30a4\u30c9: pandas \u3067 csv \u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

CSV (\u30ab\u30f3\u30de\u533a\u5207\u308a\u5024) \u30d5\u30a1\u30a4\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u3092\u4fdd\u5b58\u3059\u308b\u6700\u3082\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u306e 1 \u3064\u3067\u3059\u3002<\/span><\/p>\n

\u5e78\u3044\u306a\u3053\u3068\u306b\u3001pandas read_csv()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001Python \u3067\u5e0c\u671b\u3059\u308b\u307b\u307c\u3059\u3079\u3066\u306e\u5f62\u5f0f\u306e CSV \u30d5\u30a1\u30a4\u30eb\u3092\u7c21\u5358\u306b\u8aad\u307f\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001 \u300cdata.csv\u300d<\/strong>\u3068\u3044\u3046\u540d\u524d\u306e\u6b21\u306e CSV \u30d5\u30a1\u30a4\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001Python \u3067 CSV \u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u3044\u304f\u3064\u304b\u306e\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

 playerID,team,points\n1,Lakers,26\n2,Mavs,19\n3,Bucks,24\n4,Spurs,22<\/strong><\/pre>\n
\u4f8b 1: CSV \u30d5\u30a1\u30a4\u30eb\u3092 pandas DataFrame \u306b\u8aad\u307f\u8fbc\u307f\u307e\u3059<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001CSV \u30d5\u30a1\u30a4\u30eb\u3092 pandas DataFrame \u306b\u8aad\u307f\u53d6\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #import CSV file as DataFrame<\/span>\ndf = pd. read_csv<\/span> ('data.csv')<\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n        playerID team points\n0 1 Lakers 26\n1 2 Mavs 19\n2 3 Bucks 24\n3 4 Spurs 22<\/strong><\/span><\/pre>\n
\u4f8b 2: CSV \u30d5\u30a1\u30a4\u30eb\u304b\u3089\u7279\u5b9a\u306e\u5217\u3092\u8aad\u307f\u53d6\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001CSV \u30d5\u30a1\u30a4\u30eb\u304b\u3089\u300cplayerID\u300d\u3068\u300cpoints\u300d\u3068\u3044\u3046\u30e9\u30d9\u30eb\u306e\u4ed8\u3044\u305f\u5217\u306e\u307f\u3092 pandas DataFrame \u306b\u8aad\u307f\u53d6\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #import only specific columns from CSV file<\/span>\ndf = pd. read_csv<\/span> ('data.csv', usecols=[' playerID<\/span> ',' points<\/span> '])<\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n\tplayerID points\n0 1 26\n1 2 19\n2 3 24\n3 4 22<\/strong><\/span><\/pre>\n
 pandas DataFrame \u306b\u8aad\u307f\u8fbc\u3080\u5217\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #import only specific columns from CSV file<\/span>\ndf = pd. read_csv<\/span> (' data.csv<\/span> ', usecols=[ 0,1<\/span> ])<\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n        playerID team\n0 1 Lakers\n1 2 Mavs\n2 3 Bucks\n3 4 Spurs<\/strong><\/span><\/pre>\n
\u4f8b3\uff1aCSV\u30d5\u30a1\u30a4\u30eb\u30a4\u30f3\u30dd\u30fc\u30c8\u6642\u306e\u30d8\u30c3\u30c0\u30fc\u884c\u306e\u6307\u5b9a<\/strong><\/span><\/h2>\n
\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u3001\u30d8\u30c3\u30c0\u30fc\u884c\u304c CSV \u30d5\u30a1\u30a4\u30eb\u306e\u6700\u521d\u306e\u884c\u3067\u306f\u306a\u3044\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u30d8\u30c3\u30c0\u30fc\u884c\u304c\u5b9f\u969b\u306b\u306f 2 \u884c\u76ee\u306b\u8868\u793a\u3055\u308c\u308b\u6b21\u306e CSV \u30d5\u30a1\u30a4\u30eb\u306b\u3064\u3044\u3066\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n
 random,data,values\nplayerID,team,points\n1,Lakers,26\n2,Mavs,19\n3,Bucks,24\n4,Spurs,22<\/strong><\/span><\/pre>\n
\u3053\u306e CSV \u30d5\u30a1\u30a4\u30eb\u3092 pandas DataFrame \u306b\u8aad\u307f\u53d6\u308b\u306b\u306f\u3001\u6b21\u306e\u3088\u3046\u306bheader=1<\/strong>\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #import from CSV file and specify that header starts on second row<\/span>\ndf = pd. read_csv<\/span> ('data.csv', header= 1<\/span> )<\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n        playerID team points\n0 1 Lakers 26\n1 2 Mavs 19\n2 3 Bucks 24\n3 4 Spurs 22<\/strong><\/span><\/pre>\n
\u4f8b 4: CSV \u30d5\u30a1\u30a4\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3068\u304d\u306b\u884c\u3092\u30b9\u30ad\u30c3\u30d7\u3059\u308b<\/strong><\/span><\/h2>\n
CSV \u30d5\u30a1\u30a4\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3068\u304d\u306b\u3001 skiprows<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u884c\u3092\u7c21\u5358\u306b\u30b9\u30ad\u30c3\u30d7\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001CSV \u30d5\u30a1\u30a4\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3068\u304d\u306b 2 \u884c\u76ee\u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #import from CSV file and skip second row<\/span>\ndf = pd. read_csv<\/span> ('data.csv', skiprows=[ 1 ]<\/span><\/span> )<\/span><\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n        playerID team points\n0 2 Mavs 19\n1 3 Bucks 24\n2 4 Spurs 22<\/strong><\/span><\/pre>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001CSV \u30d5\u30a1\u30a4\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u3068\u304d\u306b 2 \u884c\u76ee\u3068<\/em>3 \u884c\u76ee\u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #import from CSV file and skip second and third rows<\/span>\ndf = pd. read_csv<\/span> ('data.csv', skiprows=[ 1,2 ]<\/span><\/span> )<\/span><\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n        playerID team points\n1 3 Bucks 24\n2 4 Spurs 22<\/strong><\/span><\/pre>\n
\u4f8b 5: \u30ab\u30b9\u30bf\u30e0\u533a\u5207\u308a\u6587\u5b57\u3092\u4f7f\u7528\u3057\u3066 CSV \u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b<\/strong><\/span><\/h2>\n
\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u3001CSV \u30d5\u30a1\u30a4\u30eb\u306b\u30ab\u30f3\u30de\u4ee5\u5916\u306e\u533a\u5207\u308a\u6587\u5b57\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001CSV \u30d5\u30a1\u30a4\u30eb\u306b\u533a\u5207\u308a\u6587\u5b57\u3068\u3057\u3066\u30a2\u30f3\u30c0\u30fc\u30b9\u30b3\u30a2\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 playerID_team_points\n1_Lakers_26\n2_Mavs_19\n3_Bucks_24\n4_Spurs_22\n<\/strong><\/span><\/pre>\n
\u3053\u306e CSV \u30d5\u30a1\u30a4\u30eb\u3092\u30d1\u30f3\u30c0\u306b\u8aad\u307f\u53d6\u308b\u306b\u306f\u3001 sep<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u3068\u304d\u306b\u4f7f\u7528\u3059\u308b\u533a\u5207\u308a\u6587\u5b57\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #import from CSV file and specify delimiter to use<\/span>\ndf = pd. read_csv<\/span> ('data.csv', sep=' _<\/span> ')<\/strong><\/span>\n\n#view DataFrame<\/span>\ndf\n\n\tplayerID team points\n0 1 Lakers 26\n1 2 Mavs 19\n2 3 Bucks 24\n3 4 Spurs 22<\/strong><\/span><\/pre>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/span><\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30d1\u30f3\u30c0\u306e\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Pandas\u3067\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u65b9\u6cd5<\/a>
Pandas \u3067 Excel \u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u65b9\u6cd5<\/a>
Pandas \u3067 TSV \u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u53d6\u308b\u65b9\u6cd5<\/a>
Pandas \u3067 HTML \u30c6\u30fc\u30d6\u30eb\u3092\u8aad\u307f\u53d6\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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