{"id":2664,"date":"2023-07-21T06:47:49","date_gmt":"2023-07-21T06:47:49","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%ae%e0%a4%a4%e0%a4%b2%e0%a4%ac-%e0%a4%95%e0%a5%87-%e0%a4%b8%e0%a4%be%e0%a4%a5-%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-fillna\/"},"modified":"2023-07-21T06:47:49","modified_gmt":"2023-07-21T06:47:49","slug":"%e0%a4%ae%e0%a4%a4%e0%a4%b2%e0%a4%ac-%e0%a4%95%e0%a5%87-%e0%a4%b8%e0%a4%be%e0%a4%a5-%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-fillna","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%ae%e0%a4%a4%e0%a4%b2%e0%a4%ac-%e0%a4%95%e0%a5%87-%e0%a4%b8%e0%a4%be%e0%a4%a5-%e0%a4%aa%e0%a4%be%e0%a4%82%e0%a4%a1%e0%a4%be-fillna\/","title":{"rendered":"\u092a\u093e\u0902\u0921\u093e: nan \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u0914\u0938\u0924 \u0938\u0947 \u0915\u0948\u0938\u0947 \u092d\u0930\u0947\u0902 (3 \u0909\u0926\u093e\u0939\u0930\u0923)"},"content":{"rendered":"

<\/p>\n

\n

\u0906\u092a \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u092c\u0926\u0932\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f fillna()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n

\u0907\u0938 \u0938\u0941\u0935\u093f\u0927\u093e \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0924\u0940\u0928 \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0924\u0930\u0940\u0915\u0947 \u092f\u0939\u093e\u0902 \u0926\u093f\u090f \u0917\u090f \u0939\u0948\u0902:<\/span><\/p>\n

\u0935\u093f\u0927\u093f 1: \u0914\u0938\u0924 \u0935\u093e\u0932\u0947 \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902<\/strong><\/span><\/p>\n

 df[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (df[' col1<\/span> ']. mean<\/span> ())<\/strong><\/pre>\n
 \u0935\u093f\u0927\u093f 2: \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 \u0915\u0908 \u0915\u0949\u0932\u092e\u094b\u0902 \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> (df[[' col1<\/span> ',' col2<\/span> ']]. mean<\/span> ())<\/strong><\/pre>\n
 \u0935\u093f\u0927\u093f 3: \u0938\u092d\u0940 \u0915\u0949\u0932\u092e\u094b\u0902 \u092e\u0947\u0902 \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902<\/strong><\/span><\/p>\n
 df = df. fillna<\/span> ( df.mean<\/span> ())<\/strong><\/pre>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u0915\u0947 \u0938\u093e\u0925 \u0935\u094d\u092f\u0935\u0939\u093e\u0930 \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create DataFrame with some NaN values<\/span>\ndf = pd. DataFrame<\/span> ({' rating<\/span> ': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   ' points<\/span> ': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19],\n                   ' assists<\/span> ': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame<\/span>\ndf\n\n        rating points assists rebounds\n0 NaN 25.0 5.0 11\n1 85.0 NaN 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 \u0909\u0926\u093e\u0939\u0930\u0923 1: \u0914\u0938\u0924 \u0935\u093e\u0932\u0947 \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902<\/strong><\/span><\/h3>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0930\u0947\u091f\u093f\u0902\u0917<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u0930\u0947\u091f\u093f\u0902\u0917<\/strong> \u0915\u0949\u0932\u092e \u0915\u0947 \u0914\u0938\u0924 \u092e\u093e\u0928 \u0915\u0947 \u0938\u093e\u0925 \u0915\u0948\u0938\u0947 \u092d\u0930\u0947\u0902:<\/span><\/p>\n
 #fill NaNs with column mean in 'rating' column<\/span>\ndf[' rating<\/span> '] = df[' rating<\/span> ']. fillna<\/span> (df[' rating<\/span> ']. mean<\/span> ())\n\n<\/span>#view updated DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 NaN 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n
 \u0930\u0947\u091f\u093f\u0902\u0917<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 \u0914\u0938\u0924 \u092e\u093e\u0928 85.125<\/strong> \u0925\u093e, \u0907\u0938\u0932\u093f\u090f \u0930\u0947\u091f\u093f\u0902\u0917<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 NaN \u092e\u093e\u0928 \u0909\u0938 \u092e\u093e\u0928 \u0938\u0947 \u092d\u0930 \u0917\u092f\u093e \u0925\u093e\u0964<\/span><\/p>\n
 \u0909\u0926\u093e\u0939\u0930\u0923 2:<\/strong> \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 \u0915\u0908 \u0915\u0949\u0932\u092e\u094b\u0902 \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902<\/strong><\/span><\/h3>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0930\u0947\u091f\u093f\u0902\u0917<\/strong> \u0914\u0930 \u092a\u0949\u0907\u0902\u091f<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u0909\u0928\u0915\u0947 \u0938\u0902\u092c\u0902\u0927\u093f\u0924 \u0915\u0949\u0932\u092e \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 \u0915\u0948\u0938\u0947 \u092a\u0949\u092a\u094d\u092f\u0941\u0932\u0947\u091f \u0915\u093f\u092f\u093e \u091c\u093e\u090f:<\/span><\/p>\n
 #fill NaNs with column means in 'rating' and 'points' columns<\/span>\ndf[[' rating<\/span> ', ' points<\/span> ']] = df[[' rating<\/span> ', ' points<\/span> ']]. fillna<\/span> (df[[' rating<\/span> ',' points<\/span> ']]. mean<\/span> ())\n\n#view updated DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 18.0 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n
 \u0917\u094d\u0930\u0947\u0921<\/strong> \u0914\u0930 \u092a\u0949\u0907\u0902\u091f<\/strong> \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u0909\u0928\u0915\u0947 \u0938\u0902\u092c\u0902\u0927\u093f\u0924 \u0915\u0949\u0932\u092e \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 \u092a\u0949\u092a\u094d\u092f\u0941\u0932\u0947\u091f \u0915\u093f\u090f \u0917\u090f \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0909\u0926\u093e\u0939\u0930\u0923 3: \u0938\u092d\u0940 \u0915\u0949\u0932\u092e\u094b\u0902 \u092e\u0947\u0902 NaN \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u0914\u0938\u0924 \u0938\u0947 \u092d\u0930\u0947\u0902<\/strong><\/h3>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u0949\u0932\u092e \u0914\u0938\u0924 \u0915\u0947 \u0938\u093e\u0925 \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u0915\u0948\u0938\u0947 \u092d\u0930\u0947\u0902:<\/span><\/p>\n
 #fill NaNs with column means in each column<\/span> \ndf = df. fillna<\/span> ( df.mean<\/span> ())\n\n#view updated DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 85.125 25.0 5.000000 11\n1 85,000 18.0 7,000000 8\n2 85.125 14.0 7.000000 10\n3 88,000 16.0 6.666667 6\n4 94,000 27.0 5,000000 6\n5 90,000 20.0 7,000000 9\n6 76,000 12.0 6,000000 6\n7 75,000 15.0 9,000000 10\n8 87,000 14.0 9,000000 10\n9 86,000 19.0 5,000000 7\n<\/strong><\/pre>\n
 \u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u0909\u0928\u0915\u0947 \u0915\u0949\u0932\u092e \u0915\u0947 \u0914\u0938\u0924 \u0938\u0947 \u092d\u0930\u0947 \u0917\u090f \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0906\u092a fillna()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u0947 \u0932\u093f\u090f \u0938\u0902\u092a\u0942\u0930\u094d\u0923 \u0911\u0928\u0932\u093e\u0907\u0928 \u0926\u0938\u094d\u0924\u093e\u0935\u0947\u091c\u093c \u092f\u0939\u093e\u0902<\/a> \u092a\u093e \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n
 \u0905\u0924\u093f\u0930\u093f\u0915\u094d\u0924 \u0938\u0902\u0938\u093e\u0927\u0928<\/strong><\/span><\/h3>\n
 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u092c\u0924\u093e\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u092a\u093e\u0902\u0921\u093e \u092e\u0947\u0902 \u0905\u0928\u094d\u092f \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0911\u092a\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902:<\/span><\/p>\n
 \u092a\u093e\u0902\u0921\u093e \u092e\u0947\u0902 \u0932\u0941\u092a\u094d\u0924 \u092e\u093e\u0928\u094b\u0902 \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902<\/a>
 \u092a\u093e\u0902\u0921\u093e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u0935\u093e\u0932\u0940 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u094b\u0902 \u0915\u094b \u0915\u0948\u0938\u0947 \u0939\u091f\u093e\u090f\u0902<\/a>
 \u092a\u093e\u0902\u0921\u093e \u092e\u0947\u0902 \u0935\u093f\u0936\u093f\u0937\u094d\u091f \u092e\u093e\u0928 \u0935\u093e\u0932\u0940 \u092a\u0902\u0915\u094d\u0924\u093f\u092f\u094b\u0902 \u0915\u094b \u0915\u0948\u0938\u0947 \u0939\u091f\u093e\u090f\u0902<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0906\u092a \u092a\u093e\u0902\u0921\u093e \u0921\u0947\u091f\u093e\u092b\u093c\u094d\u0930\u0947\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928\u094b\u0902 \u0915\u094b \u092c\u0926\u0932\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f fillna() \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964 \u0907\u0938 \u0938\u0941\u0935\u093f\u0927\u093e \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0924\u0940\u0928 \u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0924\u0930\u0940\u0915\u0947 \u092f\u0939\u093e\u0902 \u0926\u093f\u090f \u0917\u090f \u0939\u0948\u0902: \u0935\u093f\u0927\u093f 1: \u0914\u0938\u0924 \u0935\u093e\u0932\u0947 \u0915\u0949\u0932\u092e \u092e\u0947\u0902 NaN \u092e\u093e\u0928 \u092d\u0930\u0947\u0902 df[‘ col1 ‘] = df[‘ col1 ‘]. fillna (df[‘ col1 ‘]. mean ()) \u0935\u093f\u0927\u093f 2: \u0914\u0938\u0924 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