diff --git a/lec1.ipynb b/lec1.ipynb index d3e30f8..c3cec1b 100644 --- a/lec1.ipynb +++ b/lec1.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -108,13 +108,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", - "df = pd.read_csv(\"data/titanic.csv\", index_col=\"PassengerId\")\n", + "df = pd.read_csv(\"data/starbucks.csv\", index_col=\"Date\")\n", "\n", "df.to_csv(\"test.csv\")" ] @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -136,58 +136,61 @@ "output_type": "stream", "text": [ "\n", - "Index: 891 entries, 1 to 891\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Survived 891 non-null int64 \n", - " 1 Pclass 891 non-null int64 \n", - " 2 Name 891 non-null object \n", - " 3 Sex 891 non-null object \n", - " 4 Age 714 non-null float64\n", - " 5 SibSp 891 non-null int64 \n", - " 6 Parch 891 non-null int64 \n", - " 7 Ticket 891 non-null object \n", - " 8 Fare 891 non-null float64\n", - " 9 Cabin 204 non-null object \n", - " 10 Embarked 889 non-null object \n", - "dtypes: float64(2), int64(4), object(5)\n", - "memory usage: 83.5+ KB\n", - " count mean std min 25% 50% 75% max\n", - "Survived 891.0 0.383838 0.486592 0.00 0.0000 0.0000 1.0 1.0000\n", - "Pclass 891.0 2.308642 0.836071 1.00 2.0000 3.0000 3.0 3.0000\n", - "Age 714.0 29.699118 14.526497 0.42 20.1250 28.0000 38.0 80.0000\n", - "SibSp 891.0 0.523008 1.102743 0.00 0.0000 0.0000 1.0 8.0000\n", - "Parch 891.0 0.381594 0.806057 0.00 0.0000 0.0000 0.0 6.0000\n", - "Fare 891.0 32.204208 49.693429 0.00 7.9104 14.4542 31.0 512.3292\n", - " Survived Pclass Sex Age SibSp Parch Fare Cabin\n", - "PassengerId \n", - "1 0 3 male 22.0 1 0 7.2500 NaN\n", - "2 1 1 female 38.0 1 0 71.2833 C85\n", - "3 1 3 female 26.0 0 0 7.9250 NaN\n", - "4 1 1 female 35.0 1 0 53.1000 C123\n", - "5 0 3 male 35.0 0 0 8.0500 NaN\n", - " Survived Pclass Sex Age SibSp Parch Fare Cabin\n", - "PassengerId \n", - "887 0 2 male 27.0 0 0 13.00 NaN\n", - "888 1 1 female 19.0 0 0 30.00 B42\n", - "889 0 3 female NaN 1 2 23.45 NaN\n", - "890 1 1 male 26.0 0 0 30.00 C148\n", - "891 0 3 male 32.0 0 0 7.75 NaN\n", - " Survived Pclass Sex Age SibSp Parch Fare Cabin\n", - "PassengerId \n", - "804 1 3 male 0.42 0 1 8.5167 NaN\n", - "756 1 2 male 0.67 1 1 14.5000 NaN\n", - "470 1 3 female 0.75 2 1 19.2583 NaN\n", - "645 1 3 female 0.75 2 1 19.2583 NaN\n", - "79 1 2 male 0.83 0 2 29.0000 NaN\n", - " Survived Pclass Sex Age SibSp Parch Fare Cabin\n", - "PassengerId \n", - "860 0 3 male NaN 0 0 7.2292 NaN\n", - "864 0 3 female NaN 8 2 69.5500 NaN\n", - "869 0 3 male NaN 0 0 9.5000 NaN\n", - "879 0 3 male NaN 0 0 7.8958 NaN\n", - "889 0 3 female NaN 1 2 23.4500 NaN\n" + "Index: 8036 entries, 1992-06-26 to 2024-05-23\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Open 8036 non-null float64\n", + " 1 High 8036 non-null float64\n", + " 2 Low 8036 non-null float64\n", + " 3 Close 8036 non-null float64\n", + " 4 Adj Close 8036 non-null float64\n", + " 5 Volume 8036 non-null int64 \n", + "dtypes: float64(5), int64(1)\n", + "memory usage: 439.5+ KB\n", + " count mean std min 25% \\\n", + "Open 8036.0 3.005428e+01 3.361558e+01 3.281250e-01 4.392031e+00 \n", + "High 8036.0 3.035149e+01 3.390661e+01 3.476560e-01 4.531250e+00 \n", + "Low 8036.0 2.975132e+01 3.331457e+01 3.203130e-01 4.304922e+00 \n", + "Close 8036.0 3.005886e+01 3.361591e+01 3.359380e-01 4.399610e+00 \n", + "Adj Close 8036.0 2.667402e+01 3.172809e+01 2.607030e-01 3.414300e+00 \n", + "Volume 8036.0 1.470459e+07 1.340021e+07 1.504000e+06 7.817750e+06 \n", + "\n", + " 50% 75% max \n", + "Open 1.332500e+01 5.525000e+01 1.260800e+02 \n", + "High 1.349375e+01 5.572250e+01 1.263200e+02 \n", + "Low 1.315000e+01 5.485250e+01 1.248100e+02 \n", + "Close 1.333000e+01 5.526750e+01 1.260600e+02 \n", + "Adj Close 1.035245e+01 4.746483e+01 1.180104e+02 \n", + "Volume 1.169815e+07 1.778795e+07 5.855088e+08 \n", + " High Low Adj Close Volume\n", + "Date \n", + "1992-06-26 0.347656 0.320313 0.260703 224358400\n", + "1992-06-29 0.367188 0.332031 0.278891 58732800\n", + "1992-06-30 0.371094 0.343750 0.269797 34777600\n", + "1992-07-01 0.359375 0.339844 0.275860 18316800\n", + "1992-07-02 0.359375 0.347656 0.275860 13996800\n", + " High Low Adj Close Volume\n", + "Date \n", + "2024-05-17 78.000000 74.919998 77.849998 14436500\n", + "2024-05-20 78.320000 76.709999 77.540001 11183800\n", + "2024-05-21 78.220001 77.500000 77.720001 8916600\n", + "2024-05-22 81.019997 77.440002 80.720001 22063400\n", + "2024-05-23 80.699997 79.169998 79.260002 4651418\n", + " High Low Adj Close Volume\n", + "Date \n", + "1995-08-10 1.195313 1.183594 0.918523 1504000\n", + "2019-12-24 88.599998 88.000000 80.528366 1847800\n", + "1993-12-15 0.710938 0.695313 0.545657 1875200\n", + "1993-12-07 0.773438 0.750000 0.582034 1910400\n", + "2020-12-24 102.360001 101.680000 94.714470 1949200\n", + " High Low Adj Close Volume\n", + "Date \n", + "1998-07-24 3.375000 2.882813 2.322075 155107200\n", + "1992-06-26 0.347656 0.320313 0.260703 224358400\n", + "1995-09-29 1.371094 1.125000 0.918523 230883200\n", + "2000-06-07 4.671875 4.000000 3.352761 295411200\n", + "1999-07-01 3.375000 2.929688 2.613092 585508800\n" ] } ], @@ -196,11 +199,11 @@ "\n", "print(df.describe().transpose())\n", "\n", - "cleared_df = df.drop([\"Name\", \"Ticket\", \"Embarked\"], axis=1)\n", + "cleared_df = df.drop([\"Open\", \"Close\"], axis=1)\n", "print(cleared_df.head())\n", "print(cleared_df.tail())\n", "\n", - "sorted_df = cleared_df.sort_values(by=\"Age\")\n", + "sorted_df = cleared_df.sort_values(by=\"Volume\")\n", "print(sorted_df.head())\n", "print(sorted_df.tail())" ] @@ -214,104 +217,79 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "PassengerId\n", - "1 22.0\n", - "2 38.0\n", - "3 26.0\n", - "4 35.0\n", - "5 35.0\n", - " ... \n", - "887 27.0\n", - "888 19.0\n", - "889 NaN\n", - "890 26.0\n", - "891 32.0\n", - "Name: Age, Length: 891, dtype: float64\n", - "Survived 0\n", - "Pclass 2\n", - "Name Kantor, Mr. Sinai\n", - "Sex male\n", - "Age 34.0\n", - "SibSp 1\n", - "Parch 0\n", - "Ticket 244367\n", - "Fare 26.0\n", - "Cabin NaN\n", - "Embarked S\n", - "Name: 100, dtype: object\n", - "Kantor, Mr. Sinai\n", - " Age Name\n", - "PassengerId \n", - "100 34.0 Kantor, Mr. Sinai\n", - "101 28.0 Petranec, Miss. Matilda\n", - "102 NaN Petroff, Mr. Pastcho (\"Pentcho\")\n", - "103 21.0 White, Mr. Richard Frasar\n", - "104 33.0 Johansson, Mr. Gustaf Joel\n", - "... ... ...\n", - "196 58.0 Lurette, Miss. Elise\n", - "197 NaN Mernagh, Mr. Robert\n", - "198 42.0 Olsen, Mr. Karl Siegwart Andreas\n", - "199 NaN Madigan, Miss. Margaret \"Maggie\"\n", - "200 24.0 Yrois, Miss. Henriette (\"Mrs Harbeck\")\n", - "\n", - "[101 rows x 2 columns]\n", - " Survived Pclass \\\n", - "PassengerId \n", - "1 0 3 \n", - "2 1 1 \n", - "3 1 3 \n", - "\n", - " Name Sex Age \\\n", - "PassengerId \n", - "1 Braund, Mr. Owen Harris male 22.0 \n", - "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "3 Heikkinen, Miss. Laina female 26.0 \n", - "\n", - " SibSp Parch Ticket Fare Cabin Embarked \n", - "PassengerId \n", - "1 1 0 A/5 21171 7.2500 NaN S \n", - "2 1 0 PC 17599 71.2833 C85 C \n", - "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", - "Survived 0\n", - "Pclass 3\n", - "Name Braund, Mr. Owen Harris\n", - "Sex male\n", - "Age 22.0\n", - "SibSp 1\n", - "Parch 0\n", - "Ticket A/5 21171\n", - "Fare 7.25\n", - "Cabin NaN\n", - "Embarked S\n", - "Name: 1, dtype: object\n", - " Survived Pclass\n", - "PassengerId \n", - "4 1 1\n", - "5 0 3\n", - " Survived Pclass\n", - "PassengerId \n", - "4 1 1\n", - "5 0 3\n" + "Date\n", + "1992-06-26 224358400\n", + "1992-06-29 58732800\n", + "1992-06-30 34777600\n", + "1992-07-01 18316800\n", + "1992-07-02 13996800\n", + " ... \n", + "2024-05-17 14436500\n", + "2024-05-20 11183800\n", + "2024-05-21 8916600\n", + "2024-05-22 22063400\n", + "2024-05-23 4651418\n", + "Name: Volume, Length: 8036, dtype: int64\n", + "Open 5.078130e-01\n", + "High 5.078130e-01\n", + "Low 5.000000e-01\n", + "Close 5.078130e-01\n", + "Adj Close 3.940860e-01\n", + "Volume 8.256000e+06\n", + "Name: 1992-11-17, dtype: float64\n", + "0.339844\n", + " Volume Open\n", + "Date \n", + "1992-07-08 15500800 0.355469\n", + "1992-07-09 3923200 0.351563\n", + "1992-07-10 11040000 0.359375\n", + "1992-07-13 5996800 0.363281\n", + "1992-07-14 17062400 0.371094\n", + "1992-07-15 4992000 0.375000\n", + "1992-07-16 17062400 0.382813\n", + "1992-07-17 15667200 0.410156\n", + "1992-07-20 19744000 0.427734\n", + "1992-07-21 7782400 0.437500\n", + "1992-07-22 10892800 0.433594\n", + " Open High Low Close Adj Close Volume\n", + "Date \n", + "1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 58732800\n", + "1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 34777600\n", + "Open 3.281250e-01\n", + "High 3.476560e-01\n", + "Low 3.203130e-01\n", + "Close 3.359380e-01\n", + "Adj Close 2.607030e-01\n", + "Volume 2.243584e+08\n", + "Name: 1992-06-26, dtype: float64\n", + " Open High\n", + "Date \n", + "1992-07-01 0.351563 0.359375\n", + "1992-07-02 0.359375 0.359375\n", + " Open High\n", + "Date \n", + "1992-07-01 0.351563 0.359375\n", + "1992-07-02 0.359375 0.359375\n" ] } ], "source": [ - "print(df[\"Age\"])\n", + "print(df[\"Volume\"])\n", "\n", - "print(df.loc[100])\n", + "print(df.iloc[100])\n", "\n", - "print(df.loc[100, \"Name\"])\n", + "print(df.loc[\"1992-06-29\", \"Open\"]) #мда индекс\n", "\n", - "print(df.loc[100:200, [\"Age\", \"Name\"]])\n", + "print(df.loc[\"1992-07-08\":\"1992-07-22\", [\"Volume\", \"Open\"]])\n", "\n", - "print(df[0:3])\n", + "print(df[1:3])\n", "\n", "print(df.iloc[0])\n", "\n", @@ -329,39 +307,7744 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['male' 'female']\n", - "male count = 577\n", - "female count = 314\n", - "Total count = 891\n", - " Pclass Survived Count\n", - "0 1 0 80\n", - "1 1 1 136\n", - "2 2 0 97\n", - "3 2 1 87\n", - "4 3 0 372\n", - "5 3 1 119\n" + "[224358400 58732800 34777600 ... 8916600 22063400 4651418]\n", + "224358400 count = 1\n", + "58732800 count = 1\n", + "34777600 count = 1\n", + "18316800 count = 1\n", + "13996800 count = 1\n", + "5753600 count = 2\n", + "10662400 count = 1\n", + "15500800 count = 1\n", + "3923200 count = 1\n", + "11040000 count = 1\n", + "5996800 count = 2\n", + "17062400 count = 2\n", + "4992000 count = 1\n", + "15667200 count = 1\n", + "19744000 count = 2\n", + "7782400 count = 1\n", + "10892800 count = 2\n", + "10387200 count = 2\n", + "7052800 count = 1\n", + "11430400 count = 1\n", + "18009600 count = 1\n", + "30848000 count = 1\n", + "11737600 count = 1\n", + "6886400 count = 1\n", + "4832000 count = 1\n", + "6822400 count = 1\n", + "9516800 count = 2\n", + "3673600 count = 1\n", + "8825600 count = 1\n", + "5888000 count = 1\n", + "4147200 count = 1\n", + "12736000 count = 1\n", + "48320000 count = 1\n", + "17849600 count = 1\n", + "2681600 count = 1\n", + "2860800 count = 1\n", + "2380800 count = 1\n", + "2489600 count = 1\n", + "2048000 count = 1\n", + "6905600 count = 1\n", + "4128000 count = 1\n", + "19385600 count = 1\n", + "6169600 count = 1\n", + "3417600 count = 1\n", + "4441600 count = 2\n", + "2387200 count = 1\n", + "11692800 count = 1\n", + "16480000 count = 1\n", + "4249600 count = 1\n", + "17139200 count = 1\n", + "25299200 count = 1\n", + "8889600 count = 1\n", + "13036800 count = 3\n", + "13068800 count = 1\n", + "18099200 count = 1\n", + "14963200 count = 1\n", + "13459200 count = 1\n", + "17260800 count = 1\n", + "24012800 count = 2\n", + "19206400 count = 2\n", + "26912000 count = 1\n", + "5459200 count = 1\n", + "12102400 count = 1\n", + "7532800 count = 1\n", + "9593600 count = 1\n", + "11123200 count = 1\n", + "4556800 count = 1\n", + "5926400 count = 1\n", + "12755200 count = 1\n", + "6022400 count = 1\n", + "3692800 count = 1\n", + "2470400 count = 1\n", + "2432000 count = 1\n", + "4134400 count = 2\n", + "4390400 count = 1\n", + "4742400 count = 1\n", + "2950400 count = 1\n", + "8774400 count = 1\n", + "14745600 count = 1\n", + "8032000 count = 1\n", + "3366400 count = 1\n", + "5772800 count = 1\n", + "5785600 count = 1\n", + "7436800 count = 1\n", + "19462400 count = 1\n", + "22240000 count = 1\n", + "21280000 count = 1\n", + "5120000 count = 1\n", + "18604800 count = 1\n", + "5952000 count = 1\n", + "3699200 count = 2\n", + "17504000 count = 1\n", + "25056000 count = 1\n", + "13024000 count = 1\n", + "4870400 count = 1\n", + "4230400 count = 1\n", + "12524800 count = 1\n", + "11520000 count = 3\n", + "12582400 count = 1\n", + "8256000 count = 1\n", + "25280000 count = 1\n", + "19276800 count = 1\n", + "46131200 count = 1\n", + "16620800 count = 1\n", + "6067200 count = 1\n", + "8051200 count = 3\n", + "2464000 count = 1\n", + "9465600 count = 1\n", + "9171200 count = 1\n", + "44780800 count = 1\n", + "28006400 count = 1\n", + "24364800 count = 1\n", + "34515200 count = 1\n", + "16448000 count = 1\n", + "7718400 count = 2\n", + "14259200 count = 1\n", + "5734400 count = 1\n", + "7123200 count = 2\n", + "6867200 count = 1\n", + "17529600 count = 1\n", + "14988800 count = 1\n", + "4473600 count = 1\n", + "3046400 count = 1\n", + "7808000 count = 1\n", + "5420800 count = 1\n", + "13766400 count = 1\n", + "8992000 count = 1\n", + "5836800 count = 1\n", + "13376000 count = 1\n", + "14240000 count = 1\n", + "18329600 count = 1\n", + "33414400 count = 1\n", + "24044800 count = 1\n", + "15705600 count = 1\n", + "6220800 count = 1\n", + "14291200 count = 1\n", + "19065600 count = 1\n", + "32748800 count = 1\n", + "10521600 count = 1\n", + "11148800 count = 1\n", + "7315200 count = 1\n", + "15052800 count = 2\n", + "9196800 count = 2\n", + "13228800 count = 2\n", + "10803200 count = 2\n", + "10060800 count = 1\n", + "3468800 count = 1\n", + "2848000 count = 1\n", + "7974400 count = 1\n", + "17094400 count = 1\n", + "6099200 count = 1\n", + "10611200 count = 1\n", + "8742400 count = 1\n", + "6201600 count = 2\n", + "16409600 count = 2\n", + "11840000 count = 1\n", + "4608000 count = 1\n", + "3769600 count = 1\n", + "21036800 count = 1\n", + "25715200 count = 1\n", + "11219200 count = 1\n", + "9798400 count = 1\n", + "5184000 count = 2\n", + "20089600 count = 1\n", + "16672000 count = 1\n", + "10336000 count = 2\n", + "4563200 count = 1\n", + "2956800 count = 1\n", + "6374400 count = 3\n", + "6956800 count = 1\n", + "17971200 count = 1\n", + "4896000 count = 1\n", + "5075200 count = 1\n", + "5280000 count = 2\n", + "3104000 count = 1\n", + "29836800 count = 1\n", + "15372800 count = 1\n", + "8569600 count = 1\n", + "7276800 count = 1\n", + "5907200 count = 1\n", + "2547200 count = 1\n", + "2745600 count = 1\n", + "3737600 count = 2\n", + "7929600 count = 1\n", + "6816000 count = 1\n", + "4601600 count = 1\n", + "13171200 count = 1\n", + "4569600 count = 1\n", + "5478400 count = 1\n", + "4524800 count = 1\n", + "2784000 count = 1\n", + "5049600 count = 1\n", + "3001600 count = 2\n", + "23270400 count = 1\n", + "14681600 count = 1\n", + "17817600 count = 2\n", + "9100800 count = 2\n", + "6752000 count = 1\n", + "2873600 count = 1\n", + "2886400 count = 1\n", + "7936000 count = 2\n", + "8748800 count = 1\n", + "19801600 count = 1\n", + "10035200 count = 2\n", + "7628800 count = 1\n", + "6246400 count = 1\n", + "5990400 count = 2\n", + "4595200 count = 1\n", + "6297600 count = 1\n", + "5964800 count = 2\n", + "17926400 count = 1\n", + "28633600 count = 1\n", + "42073600 count = 1\n", + "12256000 count = 1\n", + "3564800 count = 1\n", + "11334400 count = 1\n", + "15891200 count = 1\n", + "19750400 count = 1\n", + "10489600 count = 1\n", + "13088000 count = 1\n", + "9683200 count = 1\n", + "7244800 count = 1\n", + "10726400 count = 1\n", + "30355200 count = 1\n", + "24134400 count = 2\n", + "15596800 count = 1\n", + "8627200 count = 2\n", + "10630400 count = 1\n", + "10636800 count = 1\n", + "10681600 count = 2\n", + "15065600 count = 1\n", + "9049600 count = 1\n", + "11008000 count = 1\n", + "4691200 count = 1\n", + "17606400 count = 1\n", + "12614400 count = 1\n", + "7712000 count = 1\n", + "14060800 count = 2\n", + "6457600 count = 1\n", + "6195200 count = 1\n", + "4025600 count = 1\n", + "14003200 count = 1\n", + "13030400 count = 1\n", + "13811200 count = 3\n", + "4998400 count = 1\n", + "6278400 count = 1\n", + "3500800 count = 1\n", + "6150400 count = 1\n", + "8140800 count = 1\n", + "12441600 count = 1\n", + "20870400 count = 1\n", + "19302400 count = 2\n", + "15660800 count = 1\n", + "10982400 count = 1\n", + "13344000 count = 1\n", + "21868800 count = 1\n", + "22316800 count = 1\n", + "20172800 count = 1\n", + "12601600 count = 1\n", + "18796800 count = 1\n", + "14739200 count = 1\n", + "11539200 count = 1\n", + "11270400 count = 1\n", + "9811200 count = 1\n", + "13286400 count = 1\n", + "14284800 count = 3\n", + "4000000 count = 1\n", + "4153600 count = 1\n", + "17254400 count = 1\n", + "35795200 count = 1\n", + "9862400 count = 1\n", + "20044800 count = 1\n", + "11974400 count = 1\n", + "12998400 count = 1\n", + "2624000 count = 1\n", + "10720000 count = 1\n", + "18336000 count = 1\n", + "6444800 count = 1\n", + "2073600 count = 1\n", + "9785600 count = 1\n", + "7392000 count = 1\n", + "24614400 count = 1\n", + "14201600 count = 1\n", + "11571200 count = 1\n", + "11225600 count = 1\n", + "11315200 count = 1\n", + "12691200 count = 1\n", + "27353600 count = 1\n", + "7801600 count = 1\n", + "24608000 count = 1\n", + "14361600 count = 1\n", + "15161600 count = 1\n", + "15987200 count = 1\n", + "21209600 count = 1\n", + "24723200 count = 2\n", + "13216000 count = 1\n", + "6419200 count = 1\n", + "12627200 count = 1\n", + "8723200 count = 1\n", + "10265600 count = 1\n", + "10572800 count = 1\n", + "11603200 count = 1\n", + "3142400 count = 1\n", + "11936000 count = 1\n", + "19296000 count = 1\n", + "29811200 count = 1\n", + "23881600 count = 1\n", + "11289600 count = 1\n", + "10928000 count = 1\n", + "9113600 count = 1\n", + "10025600 count = 1\n", + "26134400 count = 1\n", + "13430400 count = 1\n", + "16281600 count = 2\n", + "9689600 count = 1\n", + "5667200 count = 1\n", + "3968000 count = 1\n", + "12083200 count = 1\n", + "5200000 count = 1\n", + "6451200 count = 2\n", + "8924800 count = 2\n", + "9440000 count = 1\n", + "6227200 count = 1\n", + "4048000 count = 1\n", + "2019200 count = 1\n", + "2880000 count = 1\n", + "8435200 count = 2\n", + "3913600 count = 1\n", + "5088000 count = 2\n", + "5145600 count = 1\n", + "6704000 count = 2\n", + "5414400 count = 1\n", + "8393600 count = 1\n", + "2832000 count = 1\n", + "2659200 count = 1\n", + "5068800 count = 1\n", + "3136000 count = 1\n", + "2851200 count = 1\n", + "4899200 count = 1\n", + "7132800 count = 1\n", + "5001600 count = 2\n", + "20124800 count = 1\n", + "16822400 count = 1\n", + "42563200 count = 1\n", + "35849600 count = 1\n", + "11238400 count = 3\n", + "2361600 count = 1\n", + "4067200 count 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"5462400 count = 1\n", + "7968000 count = 2\n", + "4780800 count = 1\n", + "5312000 count = 1\n", + "10534400 count = 1\n", + "11078400 count = 1\n", + "13795200 count = 1\n", + "20320000 count = 1\n", + "4982400 count = 1\n", + "6928000 count = 1\n", + "10640000 count = 3\n", + "2150400 count = 1\n", + "5097600 count = 1\n", + "8342400 count = 2\n", + "11670400 count = 1\n", + "14774400 count = 1\n", + "9107200 count = 1\n", + "4838400 count = 1\n", + "6224000 count = 1\n", + "9264000 count = 1\n", + "8169600 count = 3\n", + "3206400 count = 2\n", + "6892800 count = 1\n", + "6720000 count = 1\n", + "16294400 count = 1\n", + "6729600 count = 1\n", + "7190400 count = 1\n", + "7580800 count = 1\n", + "7910400 count = 1\n", + "6710400 count = 1\n", + "6153600 count = 1\n", + "30617600 count = 1\n", + "54233600 count = 1\n", + "7513600 count = 1\n", + "4588800 count = 1\n", + "6998400 count = 1\n", + "12326400 count = 1\n", + "13628800 count = 1\n", + "6572800 count = 1\n", + "8988800 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count = 1\n", + "2966400 count = 1\n", + "3520000 count = 1\n", + "6873600 count = 1\n", + "45456000 count = 1\n", + "31564800 count = 1\n", + "17628800 count = 2\n", + "26291200 count = 1\n", + "7385600 count = 1\n", + "13411200 count = 1\n", + "6598400 count = 1\n", + "10675200 count = 1\n", + "16867200 count = 1\n", + "5776000 count = 1\n", + "3248000 count = 1\n", + "6240000 count = 1\n", + "8316800 count = 1\n", + "9184000 count = 1\n", + "3801600 count = 1\n", + "16384000 count = 1\n", + "11763200 count = 1\n", + "26864000 count = 1\n", + "20364800 count = 1\n", + "16771200 count = 2\n", + "6080000 count = 2\n", + "10912000 count = 2\n", + "11923200 count = 1\n", + "16198400 count = 1\n", + "13577600 count = 1\n", + "18848000 count = 1\n", + "46592000 count = 1\n", + "30432000 count = 1\n", + "17248000 count = 1\n", + "5897600 count = 1\n", + "18966400 count = 1\n", + "30531200 count = 1\n", + "22262400 count = 1\n", + "6137600 count = 1\n", + "33443200 count = 1\n", + "7696000 count = 2\n", + "7952000 count = 2\n", + "11449600 count = 1\n", + "3228800 count = 2\n", + "5795200 count = 1\n", + "4905600 count = 1\n", + "9513600 count = 4\n", + "4614400 count = 1\n", + "4729600 count = 1\n", + "7795200 count = 2\n", + "18800000 count = 1\n", + "8451200 count = 2\n", + "12531200 count = 1\n", + "13212800 count = 1\n", + "14067200 count = 1\n", + "19504000 count = 1\n", + "6272000 count = 1\n", + "11900800 count = 1\n", + "10915200 count = 1\n", + "14630400 count = 2\n", + "7299200 count = 1\n", + "8617600 count = 1\n", + "6185600 count = 2\n", + "12489600 count = 1\n", + "12931200 count = 1\n", + "5571200 count = 1\n", + "3532800 count = 1\n", + "13433600 count = 1\n", + "54953600 count = 1\n", + "34332800 count = 1\n", + "12041600 count = 1\n", + "23536000 count = 1\n", + "13833600 count = 1\n", + "15542400 count = 1\n", + "9654400 count = 1\n", + "6556800 count = 1\n", + "3123200 count = 1\n", + "31574400 count = 1\n", + "53222400 count = 1\n", + "43913600 count = 1\n", + "27248000 count = 1\n", + "13993600 count = 1\n", + "6092800 count = 1\n", + "10646400 count = 1\n", + "34262400 count = 1\n", + "21497600 count = 1\n", + "20492800 count = 1\n", + "14025600 count = 1\n", + "10089600 count = 1\n", + "11235200 count = 1\n", + "6960000 count = 1\n", + "26841600 count = 1\n", + "9388800 count = 1\n", + "8198400 count = 2\n", + "17459200 count = 1\n", + "6262400 count = 1\n", + "4736000 count = 1\n", + "3363200 count = 1\n", + "6256000 count = 1\n", + "13104000 count = 1\n", + "2704000 count = 1\n", + "6835200 count = 1\n", + "8320000 count = 1\n", + "29497600 count = 1\n", + "18406400 count = 1\n", + "17875200 count = 1\n", + "22345600 count = 1\n", + "9897600 count = 1\n", + "12435200 count = 1\n", + "62451200 count = 1\n", + "43059200 count = 1\n", + "17564800 count = 1\n", + "8611200 count = 1\n", + "12563200 count = 2\n", + "9238400 count = 1\n", + "7305600 count = 1\n", + "4771200 count = 1\n", + "17596800 count = 1\n", + "14076800 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1\n", + "19990400 count = 1\n", + "12323200 count = 1\n", + "19129600 count = 1\n", + "35321600 count = 1\n", + "10633600 count = 1\n", + "14876800 count = 2\n", + "50886400 count = 1\n", + "23635200 count = 1\n", + "16883200 count = 1\n", + "10294400 count = 1\n", + "10156800 count = 1\n", + "16745600 count = 2\n", + "9203200 count = 1\n", + "14755200 count = 2\n", + "37862400 count = 1\n", + "13715200 count = 1\n", + "22233600 count = 2\n", + "32624000 count = 1\n", + "16723200 count = 1\n", + "11686400 count = 2\n", + "18636800 count = 1\n", + "13920000 count = 1\n", + "9283200 count = 1\n", + "10220800 count = 1\n", + "66217600 count = 1\n", + "19625600 count = 1\n", + "12368000 count = 1\n", + "10684800 count = 1\n", + "18694400 count = 1\n", + "49625600 count = 1\n", + "94096000 count = 1\n", + "70179200 count = 1\n", + "41065600 count = 1\n", + "25811200 count = 1\n", + "11795200 count = 1\n", + "10857600 count = 1\n", + "19334400 count = 1\n", + "23891200 count = 1\n", + "12585600 count = 1\n", + "10009600 count = 1\n", + "27504000 count = 1\n", + "12560000 count = 1\n", + "32198400 count = 1\n", + "36115200 count = 1\n", + "37715200 count = 1\n", + "15158400 count = 1\n", + "35056000 count = 1\n", + "62819200 count = 1\n", + "47008000 count = 1\n", + "21612800 count = 1\n", + "22844800 count = 1\n", + "35011200 count = 1\n", + "40377600 count = 1\n", + "14684800 count = 1\n", + "49158400 count = 1\n", + "32598400 count = 1\n", + "17270400 count = 1\n", + "55404800 count = 1\n", + "27702400 count = 1\n", + "22876800 count = 1\n", + "21052800 count = 1\n", + "22060800 count = 1\n", + "15366400 count = 1\n", + "6678400 count = 1\n", + "3132800 count = 1\n", + "31881600 count = 1\n", + "55968000 count = 1\n", + "38784000 count = 2\n", + "30348800 count = 1\n", + "17452800 count = 1\n", + "18057600 count = 1\n", + "23872000 count = 1\n", + "16592000 count = 1\n", + "7520000 count = 2\n", + "13507200 count = 2\n", + "12230400 count = 1\n", + "22416000 count 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"9347300 count = 1\n", + "11084500 count = 1\n", + "12550500 count = 1\n", + "5426800 count = 1\n", + "7384400 count = 1\n", + "6678000 count = 1\n", + "6247500 count = 1\n", + "4967900 count = 1\n", + "5607500 count = 1\n", + "7799900 count = 1\n", + "7326600 count = 1\n", + "6448600 count = 1\n", + "9778500 count = 1\n", + "6870900 count = 1\n", + "5415500 count = 1\n", + "6941800 count = 1\n", + "7379900 count = 1\n", + "11475000 count = 1\n", + "5808500 count = 1\n", + "5602300 count = 2\n", + "9213200 count = 1\n", + "5627600 count = 1\n", + "10511600 count = 1\n", + "7178400 count = 1\n", + "7625700 count = 1\n", + "8122200 count = 1\n", + "11685000 count = 1\n", + "7437100 count = 1\n", + "10457200 count = 1\n", + "11278800 count = 1\n", + "8102800 count = 1\n", + "6226400 count = 1\n", + "6088800 count = 1\n", + "7156800 count = 1\n", + "9226500 count = 1\n", + "9313700 count = 1\n", + "5401400 count = 1\n", + "5215800 count = 1\n", + "12610000 count = 1\n", + "6692500 count = 1\n", + "7312300 count = 1\n", + "7576400 count = 1\n", + "6084200 count = 1\n", + "7935100 count = 1\n", + "6205800 count = 1\n", + "7818600 count = 1\n", + "5733100 count = 1\n", + "6359500 count = 1\n", + "5355400 count = 1\n", + "5293000 count = 1\n", + "5628000 count = 1\n", + "6061300 count = 1\n", + "5591900 count = 1\n", + "4635900 count = 1\n", + "6470200 count = 1\n", + "10804800 count = 1\n", + "5636600 count = 1\n", + "7609200 count = 1\n", + "5388100 count = 1\n", + "8106300 count = 1\n", + "20846500 count = 1\n", + "11184500 count = 1\n", + "7248300 count = 1\n", + "9912100 count = 1\n", + "8342300 count = 1\n", + "6381700 count = 1\n", + "8917400 count = 1\n", + "7438000 count = 1\n", + "6414000 count = 1\n", + "7423600 count = 1\n", + "5209200 count = 1\n", + "5513800 count = 1\n", + "9989500 count = 1\n", + "5559400 count = 1\n", + "4852200 count = 1\n", + "5324900 count = 1\n", + "6809100 count = 1\n", + "4740500 count = 1\n", + "5146200 count = 1\n", + "8076600 count = 2\n", + "5802600 count = 1\n", + "6539800 count = 1\n", + "4948800 count = 1\n", + "4514300 count = 1\n", + "5209500 count = 1\n", + "5150700 count = 1\n", + "12937000 count = 1\n", + "5514400 count = 1\n", + "3991800 count = 1\n", + "6523300 count = 1\n", + "12490900 count = 1\n", + "8406600 count = 1\n", + "6862200 count = 1\n", + "6972300 count = 1\n", + "6339200 count = 1\n", + "6493200 count = 1\n", + "4510700 count = 1\n", + "4843600 count = 1\n", + "10913800 count = 1\n", + "4532400 count = 1\n", + "5662600 count = 1\n", + "4764000 count = 1\n", + "5026400 count = 1\n", + "5987700 count = 1\n", + "6588300 count = 1\n", + "6719400 count = 1\n", + "9858200 count = 1\n", + "5989000 count = 1\n", + "5869000 count = 1\n", + "4382900 count = 1\n", + "6100400 count = 1\n", + "4427200 count = 1\n", + "3986000 count = 1\n", + "5611000 count = 1\n", + "4881000 count = 1\n", + "4695100 count = 1\n", + "3698700 count = 1\n", + "4359400 count = 1\n", + "4631200 count = 1\n", + "4861200 count = 1\n", + "5767500 count = 1\n", + "7136200 count = 1\n", + "4591100 count = 1\n", + "5235300 count = 1\n", + "6653400 count = 1\n", + "5699100 count = 1\n", + "8731300 count = 1\n", + "14008800 count = 1\n", + "10658500 count = 1\n", + "8271200 count = 1\n", + "7054400 count = 1\n", + "6365300 count = 1\n", + "7221400 count = 1\n", + "7054800 count = 1\n", + "5828200 count = 1\n", + "8551400 count = 1\n", + "7407900 count = 1\n", + "5285100 count = 1\n", + "7533000 count = 1\n", + "6432200 count = 1\n", + "6008400 count = 1\n", + "6248700 count = 1\n", + "8300500 count = 1\n", + "6080400 count = 1\n", + "5450400 count = 1\n", + "7651500 count = 1\n", + "7502400 count = 1\n", + "3857000 count = 1\n", + "6425800 count = 1\n", + "6049800 count = 1\n", + "5940300 count = 1\n", + "5170800 count = 1\n", + "8499800 count = 1\n", + "7164700 count = 1\n", + "7944800 count = 1\n", + "4921900 count = 1\n", + "10282100 count = 1\n", + "6714100 count = 1\n", + "6705600 count = 1\n", + "7296900 count = 1\n", + "5859200 count = 1\n", + "6022100 count = 1\n", + "14271500 count = 1\n", + "4349700 count = 1\n", + "1847800 count = 1\n", + "4177700 count = 1\n", + "4129300 count = 1\n", + "4655600 count = 1\n", + "4115600 count = 1\n", + "6473800 count = 1\n", + "4874100 count = 1\n", + "5409800 count = 1\n", + "6514700 count = 1\n", + "7296300 count = 1\n", + "9443500 count = 1\n", + "7097900 count = 1\n", + "7382500 count = 1\n", + "5855200 count = 1\n", + "6145300 count = 1\n", + "5180300 count = 1\n", + "6962800 count = 1\n", + "6157400 count = 1\n", + "6100600 count = 1\n", + "7815500 count = 1\n", + "11066500 count = 1\n", + "11922400 count = 1\n", + "17678700 count = 1\n", + "12224900 count = 1\n", + "11693900 count = 1\n", + "9090600 count = 1\n", + "9997100 count = 1\n", + "9982700 count = 1\n", + "8901400 count = 1\n", + "8073900 count = 1\n", + "7289300 count = 1\n", + "5098500 count = 1\n", + "7747800 count = 1\n", + "8576600 count = 1\n", + "5686900 count = 1\n", + "5288800 count = 1\n", + "5210800 count = 1\n", + "5969200 count = 1\n", + "5800500 count = 1\n", + "8313600 count = 1\n", + "9404000 count = 1\n", + "10541500 count = 1\n", + "17120800 count = 1\n", + "19108600 count = 1\n", + "14258700 count = 1\n", + "12395900 count = 1\n", + "10999600 count = 1\n", + "12545300 count = 1\n", + "18080100 count = 1\n", + "15930300 count = 1\n", + "18414000 count = 1\n", + "17138000 count = 1\n", + "25170600 count = 1\n", + "23354300 count = 1\n", + "25045200 count = 1\n", + "28770600 count = 1\n", + "25167400 count = 1\n", + "28420600 count = 1\n", + "24905800 count = 1\n", + "28286200 count = 1\n", + "22977200 count = 1\n", + "20997500 count = 1\n", + "18950500 count = 1\n", + "14199700 count = 1\n", + "20109800 count = 1\n", + "12880400 count = 1\n", + "18175000 count = 1\n", + "14676500 count = 1\n", + "18010800 count = 1\n", + "13799400 count = 1\n", + "19748100 count = 1\n", + "11191800 count = 1\n", + "10642500 count = 1\n", + "11946800 count = 1\n", + "14268400 count = 1\n", + "9684700 count = 1\n", + "10345800 count = 1\n", + "11347000 count = 1\n", + "15513400 count = 1\n", + "8456000 count = 1\n", + "8258500 count = 1\n", + "12801400 count = 1\n", + "24575500 count = 1\n", + "15186000 count = 1\n", + "11216700 count = 1\n", + "12921800 count = 1\n", + "12740900 count = 1\n", + "13360500 count = 1\n", + "11917200 count = 1\n", + "9004500 count = 1\n", + "8549900 count = 1\n", + "11532700 count = 1\n", + "11932300 count = 1\n", + "9489100 count = 1\n", + "8903200 count = 1\n", + "6484900 count = 1\n", + "7790500 count = 1\n", + "10312600 count = 1\n", + "7653300 count = 1\n", + "8422200 count = 1\n", + "8331400 count = 1\n", + "10043600 count = 1\n", + "7922000 count = 1\n", + "9521000 count = 1\n", + "9956700 count = 1\n", + "7667600 count = 1\n", + "12030800 count = 1\n", + "10229600 count = 1\n", + "7999800 count = 1\n", + "19683800 count = 1\n", + "26422200 count = 1\n", + "18529800 count = 1\n", + "14644700 count = 1\n", + "10890100 count = 1\n", + "6906700 count = 1\n", + "8486000 count = 1\n", + "12538600 count = 1\n", + "8238600 count = 1\n", + "12306400 count = 1\n", + "8624800 count = 1\n", + "17627800 count = 1\n", + "8653000 count = 1\n", + "6750000 count = 1\n", + "6544400 count = 1\n", + "7235300 count = 1\n", + "7515100 count = 1\n", + "7346200 count = 1\n", + "9618200 count = 1\n", + "7524200 count = 1\n", + "9464500 count = 1\n", + "8780900 count = 1\n", + "10300700 count = 1\n", + "6649300 count = 1\n", + "6087500 count = 1\n", + "8832700 count = 1\n", + "10637500 count = 1\n", + "9213100 count = 1\n", + "7795100 count = 1\n", + "5673400 count = 1\n", + "6771100 count = 1\n", + "12273400 count = 1\n", + "15261100 count = 1\n", + "7889800 count = 1\n", + "7423800 count = 1\n", + "7138300 count = 1\n", + "12792400 count = 1\n", + "7780800 count = 1\n", + "6764800 count = 1\n", + "6569300 count = 1\n", + "6604300 count = 1\n", + "10238500 count = 1\n", + "5378000 count = 1\n", + "4383400 count = 1\n", + "3771300 count = 1\n", + "5360400 count = 2\n", + "5272700 count = 1\n", + "6840600 count = 1\n", + "5099500 count = 1\n", + "5594100 count = 1\n", + "5867400 count = 1\n", + "17659500 count = 1\n", + "7934300 count = 1\n", + "9099200 count = 1\n", + "9303900 count = 1\n", + "6401600 count = 1\n", + "8166100 count = 1\n", + "11378900 count = 1\n", + "11036600 count = 1\n", + "10349400 count = 1\n", + "11083100 count = 1\n", + "6928700 count = 1\n", + "7103300 count = 1\n", + "5535800 count = 1\n", + "7386600 count = 1\n", + "10113400 count = 1\n", + "7188800 count = 1\n", + "4465000 count = 1\n", + "6414700 count = 1\n", + "4881500 count = 1\n", + "5981500 count = 1\n", + "4064000 count = 1\n", + "10134400 count = 1\n", + "7241200 count = 1\n", + "7004800 count = 1\n", + "7951100 count = 1\n", + "5179300 count = 1\n", + "4958800 count = 1\n", + "6197700 count = 1\n", + "5909100 count = 1\n", + "5732000 count = 1\n", + "4760700 count = 1\n", + "5002200 count = 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count = 1\n", + "5156200 count = 1\n", + "5195200 count = 1\n", + "6409300 count = 1\n", + "4535700 count = 1\n", + "10215000 count = 1\n", + "7175900 count = 1\n", + "4302700 count = 1\n", + "3817300 count = 1\n", + "1949200 count = 1\n", + "5055200 count = 1\n", + "4780900 count = 1\n", + "3654100 count = 1\n", + "3566300 count = 1\n", + "8041500 count = 1\n", + "5721900 count = 1\n", + "5017200 count = 1\n", + "5057000 count = 1\n", + "5869700 count = 1\n", + "4257400 count = 1\n", + "4332600 count = 1\n", + "4804500 count = 1\n", + "4528300 count = 1\n", + "5522900 count = 1\n", + "6519600 count = 1\n", + "5844300 count = 1\n", + "5731500 count = 1\n", + "5231600 count = 1\n", + "5009000 count = 1\n", + "7195300 count = 1\n", + "18139500 count = 1\n", + "8868200 count = 1\n", + "8203000 count = 1\n", + "7206300 count = 1\n", + "6917700 count = 1\n", + "3934800 count = 1\n", + "6926800 count = 1\n", + "8072100 count = 1\n", + "4836600 count = 1\n", + "4095200 count = 1\n", + 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1\n", + "9839700 count = 1\n", + "6472300 count = 1\n", + "4358300 count = 1\n", + "4361700 count = 1\n", + "5976000 count = 1\n", + "4719800 count = 1\n", + "4520500 count = 1\n", + "5317100 count = 1\n", + "3486300 count = 1\n", + "4235100 count = 1\n", + "3447300 count = 1\n", + "3881500 count = 1\n", + "5188200 count = 1\n", + "4204700 count = 1\n", + "3078200 count = 1\n", + "6995500 count = 1\n", + "4402700 count = 1\n", + "3801900 count = 1\n", + "2921300 count = 1\n", + "4264900 count = 1\n", + "5548400 count = 1\n", + "4711200 count = 1\n", + "6517900 count = 1\n", + "4381100 count = 1\n", + "4722800 count = 1\n", + "11556200 count = 1\n", + "7997000 count = 1\n", + "9261000 count = 1\n", + "5468400 count = 1\n", + "4578200 count = 1\n", + "3842000 count = 1\n", + "3955000 count = 1\n", + "3988800 count = 1\n", + "5917700 count = 2\n", + "4389100 count = 1\n", + "5636900 count = 1\n", + "4497700 count = 1\n", + "5884000 count = 1\n", + "5734500 count = 1\n", + "5549500 count = 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+ "6849000 count = 1\n", + "7161000 count = 1\n", + "9353700 count = 1\n", + "9098800 count = 1\n", + "6924800 count = 1\n", + "7948500 count = 1\n", + "7722200 count = 1\n", + "6545800 count = 1\n", + "6578200 count = 1\n", + "6210600 count = 1\n", + "8622500 count = 1\n", + "9674800 count = 1\n", + "9260500 count = 1\n", + "7039400 count = 1\n", + "8591500 count = 1\n", + "10721100 count = 1\n", + "6587400 count = 1\n", + "7179800 count = 1\n", + "14608700 count = 1\n", + "10692000 count = 1\n", + "8811600 count = 1\n", + "9114300 count = 1\n", + "15351700 count = 1\n", + "18199300 count = 1\n", + "12542300 count = 1\n", + "21207900 count = 1\n", + "23784200 count = 1\n", + "11925300 count = 1\n", + "8447400 count = 1\n", + "7670500 count = 1\n", + "6163400 count = 1\n", + "7048100 count = 1\n", + "5981000 count = 1\n", + "8122100 count = 1\n", + "11306100 count = 1\n", + "6516300 count = 1\n", + "16243100 count = 1\n", + "17525800 count = 1\n", + "10567000 count = 1\n", + "10869100 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1\n", + "5465100 count = 1\n", + "7999600 count = 1\n", + "5624000 count = 1\n", + "7244400 count = 1\n", + "13862300 count = 1\n", + "5771100 count = 1\n", + "4533500 count = 1\n", + "5566300 count = 1\n", + "4756900 count = 1\n", + "3944500 count = 1\n", + "4402500 count = 1\n", + "3491500 count = 1\n", + "3976100 count = 1\n", + "3988900 count = 1\n", + "6607700 count = 1\n", + "8166600 count = 1\n", + "5575100 count = 1\n", + "9644100 count = 1\n", + "10124800 count = 1\n", + "4183600 count = 1\n", + "5634400 count = 1\n", + "4882000 count = 1\n", + "5529700 count = 1\n", + "5725800 count = 1\n", + "5115800 count = 1\n", + "6543500 count = 1\n", + "5517200 count = 1\n", + "5416900 count = 1\n", + "6324100 count = 1\n", + "6851100 count = 1\n", + "6880700 count = 1\n", + "5816300 count = 1\n", + "5841200 count = 1\n", + "9852900 count = 1\n", + "15200500 count = 1\n", + "6392200 count = 1\n", + "6207500 count = 1\n", + "5557500 count = 1\n", + "5608400 count = 1\n", + "6298600 count = 1\n", + "5192400 count = 1\n", + "5109400 count = 1\n", + "4059100 count = 1\n", + "4596800 count = 1\n", + "6876400 count = 1\n", + "5438000 count = 1\n", + "7523700 count = 1\n", + "6857900 count = 1\n", + "6019700 count = 1\n", + "5235100 count = 1\n", + "5186500 count = 1\n", + "5382200 count = 1\n", + "4777600 count = 1\n", + "4882100 count = 1\n", + "5237100 count = 1\n", + "3976300 count = 1\n", + "4225500 count = 1\n", + "5732300 count = 1\n", + "6641900 count = 1\n", + "6408200 count = 1\n", + "6248800 count = 1\n", + "6603300 count = 1\n", + "8188600 count = 1\n", + "10189700 count = 1\n", + "4864000 count = 1\n", + "4743700 count = 1\n", + "4685400 count = 1\n", + "6022200 count = 1\n", + "4709800 count = 1\n", + "4946600 count = 1\n", + "4082600 count = 1\n", + "6900100 count = 1\n", + "3855400 count = 1\n", + "3854400 count = 1\n", + "5170900 count = 1\n", + "3155700 count = 1\n", + "3761600 count = 1\n", + "4010600 count = 1\n", + "4606000 count = 1\n", + "4367600 count = 1\n", + "3963300 count = 1\n", + "4780700 count = 1\n", + "5300500 count = 1\n", + "4617800 count = 1\n", + "4297200 count = 1\n", + "4522800 count = 1\n", + "5940700 count = 1\n", + "8747100 count = 1\n", + "8081600 count = 1\n", + "5973400 count = 1\n", + "6495900 count = 1\n", + "9626400 count = 1\n", + "24388600 count = 1\n", + "8858100 count = 1\n", + "6090700 count = 1\n", + "3682400 count = 1\n", + "4565000 count = 1\n", + "4791700 count = 1\n", + "5167600 count = 1\n", + "4217000 count = 1\n", + "3676600 count = 1\n", + "4757300 count = 1\n", + "5377500 count = 1\n", + "4984700 count = 1\n", + "7504300 count = 1\n", + "6766300 count = 1\n", + "6134800 count = 1\n", + "8177000 count = 1\n", + "7276500 count = 1\n", + "6505400 count = 1\n", + "10377100 count = 1\n", + "6752200 count = 1\n", + "6220300 count = 1\n", + "6536500 count = 1\n", + "8657100 count = 1\n", + "6285600 count = 1\n", + "6877800 count = 1\n", + "5550800 count = 1\n", + "6705800 count = 1\n", + "5137900 count = 1\n", + "9279500 count = 1\n", + "6448200 count = 1\n", + "11708300 count = 1\n", + "5506400 count = 1\n", + "6125500 count = 1\n", + "18770100 count = 1\n", + "6069000 count = 1\n", + "5034100 count = 1\n", + "6581900 count = 1\n", + "5113500 count = 1\n", + "6228900 count = 1\n", + "3287000 count = 1\n", + "6365400 count = 1\n", + "7153300 count = 2\n", + "7887400 count = 1\n", + "9294600 count = 1\n", + "5271700 count = 1\n", + "6971900 count = 1\n", + "4511700 count = 1\n", + "3934400 count = 1\n", + "5244500 count = 1\n", + "5991000 count = 1\n", + "5556200 count = 1\n", + "5466100 count = 1\n", + "20369100 count = 1\n", + "4746600 count = 1\n", + "5627500 count = 1\n", + "6568300 count = 1\n", + "5373400 count = 1\n", + "5320700 count = 1\n", + "6377400 count = 1\n", + "7784300 count = 1\n", + "14463100 count = 1\n", + "5547500 count = 1\n", + "5196700 count = 1\n", + "5222700 count = 1\n", + "4058400 count = 1\n", + "4494300 count = 1\n", + "6844200 count = 1\n", + "3179700 count = 1\n", + "3239300 count = 1\n", + "3203700 count = 1\n", + "3679500 count = 1\n", + "5291100 count = 1\n", + "4125500 count = 1\n", + "3753200 count = 1\n", + "3640500 count = 1\n", + "9704500 count = 1\n", + "7824500 count = 1\n", + "5098900 count = 1\n", + "5512300 count = 1\n", + "4212200 count = 1\n", + "5624500 count = 1\n", + "4747100 count = 1\n", + "5414300 count = 1\n", + "5428400 count = 1\n", + "6404100 count = 1\n", + "4754700 count = 1\n", + "5577500 count = 1\n", + "5007600 count = 1\n", + "5830200 count = 1\n", + "6039700 count = 1\n", + "8766700 count = 1\n", + "6803000 count = 1\n", + "4572100 count = 1\n", + "5118900 count = 1\n", + "4818800 count = 1\n", + "6086600 count = 1\n", + "4731500 count = 1\n", + "4736300 count = 1\n", + "7698600 count = 1\n", + "6430500 count = 1\n", + "6666600 count = 1\n", + "6166100 count = 1\n", + "7548600 count = 1\n", + "6978900 count = 1\n", + "4120700 count = 1\n", + "4974800 count = 1\n", + "5485200 count = 1\n", + "4780100 count = 1\n", + "4595900 count = 1\n", + "6086700 count = 1\n", + "4110700 count = 1\n", + "5610800 count = 1\n", + "4698000 count = 1\n", + "4481000 count = 1\n", + "3600200 count = 1\n", + "5497300 count = 1\n", + "6201700 count = 1\n", + "8293600 count = 1\n", + "7852700 count = 1\n", + "9308300 count = 1\n", + "14292500 count = 1\n", + "23396300 count = 1\n", + "15994400 count = 1\n", + "7532000 count = 1\n", + "6239500 count = 1\n", + "7453000 count = 1\n", + "7607000 count = 1\n", + "7338500 count = 1\n", + "7120800 count = 1\n", + "8323000 count = 1\n", + "7980200 count = 1\n", + "8037200 count = 1\n", + "6341800 count = 1\n", + "9799500 count = 1\n", + "6149000 count = 1\n", + "4828700 count = 1\n", + "7853600 count = 1\n", + "6848100 count = 1\n", + "8428500 count = 1\n", + "11442600 count = 1\n", + "12503900 count = 1\n", + "11477400 count = 1\n", + "16027300 count = 1\n", + "12115000 count = 1\n", + "7858300 count = 1\n", + "7124400 count = 1\n", + "7639100 count = 1\n", + "6313800 count = 1\n", + "7741900 count = 1\n", + "9764800 count = 1\n", + "12463800 count = 1\n", + "6146400 count = 1\n", + "5992900 count = 1\n", + "10057400 count = 1\n", + "7097200 count = 1\n", + "6360400 count = 1\n", + "3709500 count = 1\n", + "4959300 count = 1\n", + "4518300 count = 1\n", + "6134000 count = 1\n", + "8859700 count = 1\n", + "7161700 count = 1\n", + "7118600 count = 1\n", + "7189900 count = 1\n", + "7536900 count = 1\n", + "6348500 count = 1\n", + "5847700 count = 1\n", + "6483600 count = 1\n", + "5638700 count = 1\n", + "6672900 count = 1\n", + "7549500 count = 1\n", + "9556100 count = 1\n", + "9421000 count = 1\n", + "10227100 count = 1\n", + "7661400 count = 1\n", + "11991500 count = 1\n", + "9229400 count = 1\n", + "12728100 count = 1\n", + "17625200 count = 1\n", + "26751800 count = 1\n", + "15001500 count = 1\n", + "11365900 count = 1\n", + "7335800 count = 1\n", + "11531500 count = 1\n", + "9078000 count = 1\n", + "9888100 count = 1\n", + "9385600 count = 1\n", + "9224400 count = 1\n", + "8138500 count = 1\n", + "5510600 count = 1\n", + "8878000 count = 1\n", + "5734200 count = 1\n", + "8844500 count = 1\n", + "8286300 count = 1\n", + "8124700 count = 1\n", + "6845200 count = 1\n", + "7043800 count = 1\n", + "5356100 count = 1\n", + "5622400 count = 1\n", + "11221100 count = 1\n", + "7990800 count = 1\n", + "7321000 count = 1\n", + "7137300 count = 1\n", + "4826900 count = 1\n", + "8792400 count = 1\n", + "7671500 count = 1\n", + "6702300 count = 1\n", + "9444900 count = 1\n", + "8254500 count = 1\n", + "8544800 count = 1\n", + "18133600 count = 1\n", + "7413900 count = 2\n", + "5676500 count = 1\n", + "5804400 count = 1\n", + "6287300 count = 1\n", + "6493800 count = 1\n", + "6903000 count = 1\n", + "6216100 count = 1\n", + "5863700 count = 1\n", + "6784300 count = 1\n", + "4990200 count = 1\n", + "8276800 count = 1\n", + "6316400 count = 1\n", + "6322900 count = 1\n", + "6242700 count = 1\n", + "6841600 count = 1\n", + "8591200 count = 1\n", + "8866800 count = 1\n", + "6870100 count = 1\n", + "11403300 count = 1\n", + "11898200 count = 1\n", + "8669000 count = 1\n", + "5670500 count = 1\n", + "7183900 count = 1\n", + "7284600 count = 1\n", + "7185300 count = 1\n", + "14538400 count = 1\n", + "66610700 count = 1\n", + "23883400 count = 1\n", + "22153800 count = 1\n", + "18632200 count = 1\n", + "21901300 count = 1\n", + "24482600 count = 1\n", + "18591500 count = 1\n", + "15761900 count = 1\n", + "10953600 count = 1\n", + "10961100 count = 1\n", + "8826500 count = 1\n", + "10758500 count = 1\n", + "14436500 count = 1\n", + "11183800 count = 1\n", + "8916600 count = 1\n", + "22063400 count = 1\n", + "4651418 count = 1\n", + "Total count = 8036\n", + " Date Open Count\n", + "0 1992-06-26 0.328125 1\n", + "1 1992-06-29 0.339844 1\n", + "2 1992-06-30 0.367188 1\n", + "3 1992-07-01 0.351563 1\n", + "4 1992-07-02 0.359375 1\n", + "... ... ... ...\n", + "8031 2024-05-17 75.269997 1\n", + "8032 2024-05-20 77.680000 1\n", + "8033 2024-05-21 77.559998 1\n", + "8034 2024-05-22 77.699997 1\n", + "8035 2024-05-23 80.099998 1\n", + "\n", + "[8036 rows x 3 columns]\n" ] } ], "source": [ - "s_values = df[\"Sex\"].unique()\n", + "s_values = df[\"Volume\"].unique()\n", "print(s_values)\n", "\n", "s_total = 0\n", "for s_value in s_values:\n", - " count = df[df[\"Sex\"] == s_value].shape[0]\n", + " count = df[df[\"Volume\"] == s_value].shape[0]\n", " s_total += count\n", " print(s_value, \"count =\", count)\n", "print(\"Total count = \", s_total)\n", "\n", - "print(df.groupby([\"Pclass\", \"Survived\"]).size().reset_index(name=\"Count\")) # type: ignore" + "print(df.groupby([\"Date\", \"Open\"]).size().reset_index(name=\"Count\")) # type: ignore" ] }, { @@ -373,34 +8056,34 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Pclass Survived Age\n", - "PassengerId \n", - "1 3 0 22.0\n", - "2 1 1 38.0\n", - "3 3 1 26.0\n", - "4 1 1 35.0\n", - "5 3 0 35.0\n", - "... ... ... ...\n", - "886 3 0 39.0\n", - "887 2 0 27.0\n", - "888 1 1 19.0\n", - "890 1 1 26.0\n", - "891 3 0 32.0\n", + " Volume Open Low\n", + "Date \n", + "1992-06-26 224358400 0.328125 0.320313\n", + "1992-06-29 58732800 0.339844 0.332031\n", + "1992-06-30 34777600 0.367188 0.343750\n", + "1992-07-01 18316800 0.351563 0.339844\n", + "1992-07-02 13996800 0.359375 0.347656\n", + "... ... ... ...\n", + "2024-05-17 14436500 75.269997 74.919998\n", + "2024-05-20 11183800 77.680000 76.709999\n", + "2024-05-21 8916600 77.559998 77.500000\n", + "2024-05-22 22063400 77.699997 77.440002\n", + "2024-05-23 4651418 80.099998 79.169998\n", "\n", - "[714 rows x 3 columns]\n" + "[8036 rows x 3 columns]\n" ] } ], "source": [ - "data = df[[\"Pclass\", \"Survived\", \"Age\"]].copy()\n", - "data.dropna(subset=[\"Age\"], inplace=True)\n", + "data = df[[\"Volume\", \"Open\", \"Low\"]].copy()\n", + "data.dropna(subset=[\"Low\"], inplace=True)\n", "print(data)" ] }, @@ -415,40 +8098,75 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Age \n", - " min q1 q2 median q3 max\n", - "Pclass \n", - "1 0.92 27.0 37.0 37.0 49.0 80.0\n", - "2 0.67 23.0 29.0 29.0 36.0 70.0\n", - "3 0.42 18.0 24.0 24.0 32.0 74.0\n", - " Age \n", - " low_iqr iqr high_iqr\n", - "Pclass \n", - "1 0.0 22.0 82.0\n", - "2 3.5 13.0 55.5\n", - "3 0.0 14.0 53.0\n" + " Low \\\n", + " min q1 q2 median q3 \n", + "Open \n", + "0.328125 0.320313 0.320313 0.320313 0.320313 0.320313 \n", + "0.339844 0.332031 0.332031 0.332031 0.332031 0.332031 \n", + "0.351563 0.339844 0.343750 0.347656 0.347656 0.347656 \n", + "0.355469 0.343750 0.344726 0.345703 0.345703 0.346680 \n", + "0.359375 0.347656 0.348633 0.349610 0.349610 0.350586 \n", + "... ... ... ... ... ... \n", + "122.559998 121.389999 121.389999 121.389999 121.389999 121.389999 \n", + "122.930000 122.139999 122.139999 122.139999 122.139999 122.139999 \n", + "124.550003 123.919998 123.919998 123.919998 123.919998 123.919998 \n", + "125.739998 124.250000 124.250000 124.250000 124.250000 124.250000 \n", + "126.080002 124.809998 124.809998 124.809998 124.809998 124.809998 \n", + "\n", + " \n", + " max \n", + "Open \n", + "0.328125 0.320313 \n", + "0.339844 0.332031 \n", + "0.351563 0.347656 \n", + "0.355469 0.347656 \n", + "0.359375 0.351563 \n", + "... ... \n", + "122.559998 121.389999 \n", + "122.930000 122.139999 \n", + "124.550003 123.919998 \n", + "125.739998 124.250000 \n", + "126.080002 124.809998 \n", + "\n", + "[5300 rows x 6 columns]\n", + " Open \n", + " low_iqr iqr high_iqr\n", + "Low \n", + "0.320313 0.328125 0.000000 0.328125\n", + "0.332031 0.339844 0.000000 0.339844\n", + "0.339844 0.351563 0.000000 0.351563\n", + "0.343750 0.349609 0.005860 0.373048\n", + "0.347656 0.344239 0.004882 0.363769\n", + "... ... ... ...\n", + "121.389999 122.559998 0.000000 122.559998\n", + "122.139999 122.930000 0.000000 122.930000\n", + "123.919998 124.550003 0.000000 124.550003\n", + "124.250000 125.739998 0.000000 125.739998\n", + "124.809998 126.080002 0.000000 126.080002\n", + "\n", + "[5223 rows x 3 columns]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -483,13 +8201,13 @@ " return q3(x) + 1.5 * iqr(x)\n", "\n", "\n", - "quantiles = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n", + "quantiles = data[[\"Low\", \"Open\"]].groupby([\"Open\"]).aggregate([\"min\", q1, q2, \"median\", q3, \"max\"])\n", "print(quantiles)\n", "\n", - "iqrs = data[[\"Pclass\", \"Age\"]].groupby([\"Pclass\"]).aggregate([low_iqr, iqr, high_iqr])\n", + "iqrs = data[[\"Open\", \"Low\"]].groupby([\"Low\"]).aggregate([low_iqr, iqr, high_iqr])\n", "print(iqrs)\n", "\n", - "data.boxplot(column=\"Age\", by=\"Pclass\")" + "data.boxplot(column=\"Open\", by=\"Low\")" ] }, { @@ -501,7 +8219,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -510,13 +8228,13 @@ "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -526,7 +8244,7 @@ } ], "source": [ - "data.plot.hist(column=[\"Age\"], bins=80)" + "data.plot.hist(column=[\"Open\"], bins=80)" ] }, { @@ -538,22 +8256,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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IAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsECIAgAAsGAVolatWuX1vjfeeMO6GAAAgIrCKkTddNNNGjlypH766Sdn27Fjx3Trrbdq9OjRpVYcAABAeWX9SdSSJUvUvn17bd++XcuXL1fr1q2Vl5en9PT0Ui4RAACg/LEKUZ06dVJ6erpat26tdu3a6fbbb9eIESO0evVqNWrUqLRrBAAAKHesTyzfuXOn0tLS1LBhQwUFBWnHjh0qKCgozdoAAADKLasQ9fLLLyslJUU33HCDtm7dqvXr12vTpk1KTEzUunXrSrtGAACAcscqRE2dOlVLly7V9OnTVaVKFbVu3Vrr16/XHXfcoeuuu66USwQAACh/gmwe9O233yomJsalLTg4WJMmTdItt9xSKoUBAACUZ1afRMXExCgnJ0dvv/22xowZoxMnTkiSNm7cqISEhFItEAAAoDyy+iRqy5Yt6tGjhyIjI7V3714NHjxYNWvW1IcffqisrCzNmzevtOsEAAAoV6w+iRoxYoQGDRqkjIwMValSxdl+880364svvii14gAAAMorq0+i0tLS9Oabb7q1N2jQQIcOHbrsogAAAMo7q0+iQkNDlZeX59a+c+dO1apV67KLAgAAKO+sQtRtt92m559/3vm38xwOh7KysjRq1CjdeeedpVogAABAeWQVoiZPnqxTp06pdu3aOn36tLp166amTZsqPDxcEyZMKO0aAQAAyh2rc6IiIyO1YsUKrVmzRlu2bNGpU6d09dVXq3v37qVdHwAAQLnk0ydR69at00cffeS83aVLF1WrVk2vvfaa+vXrp4ceekhnzpwp9SIBAADKG59C1PPPP69t27Y5b3/77bcaPHiwbrjhBo0ePVp///vfNXHixFIvEgAAoLzxKUSlp6e7/Mpu/vz56tChg9566y09/vjjmjZtmhYuXFjqRQIAAJQ3PoWo7Oxs1alTx3k7NTVVvXr1ct5u37699u/fX3rVAQAAlFM+hag6deooMzNTknT27Flt3LhRHTt2dN5/8uRJBQcHl26FAAAA5ZBPIermm2/W6NGj9eWXX2rMmDEKCwtT165dnfdv2bJFTZs2LfUiAQAAyhufLnHwwgsv6I477lC3bt0UHh6uuXPnKiQkxHn/7Nmz1bNnz1IvEgAAoLxxGGOMrw/Kzc1VeHi4AgMDXdpPnDih8PBwl2D1W5WXl6fIyEjl5uYqIiLC3+UAAIAS8OX92/pim57UrFnTZjgAAIAKx+rPvgAAAPzWEaIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIAAAAsEKIkDRo0SH369PF3GQAAoAIJ8ncB8N2eo6f0deZx7Tx8Ul98d0y5P57V9S3raNLdbZx9ZqzM0Nrdx9S1WS09en2C1zZJen7ZNn2155i6JNTS2FtaXfK5950oUOPoaoqPqeZsT91xROnf56hdXA11bVar2HZvdXgbe8H6LK3LPK7OTWN0d3JssX298VSHtzG6T1qlfdkFio+uphVPXOdsf2juBn2zP1vtG9XUzAHJkqSOE1bo8MmzqhcRqq+e6uHse8XYf+j0OaOwIIe2v3izs71o//cGd9S+EwV69N00nT5nFB4coK0v9HL2TXrun8o+fU41qwZp4/gbne03/yVVu47lq3ntcE37fTvtO1Ggx+dvUvbpc6pdLVjzH+7knNfNU1IvWceFuru+vFI/5P6o2KiqSh31O49zeX1AsvN1HLkw3W2M5Bc+1bH8n9zqGPJumrPmj4Zd63GOix/trH0nCjTp438r83iBrqwXocWPdva4Xd64L7nYsT2N2zi6mt79aq/Hff2JBen6eu9xpTSJcR5H/d9cpy0HctW2YZTefbCjx/2gX4c45+tRWGjc9rGi26rovD3t097qKNp2S2I953McyD7tcQxP+6m3Y9yXNcFTbcWN4Ym3Oooei8YY57/X7zle4mPflzXI2+vva7sn3uoojXXMF0XraBBV1fk8WcfzPdbni7Ksu6LV4TDGGL88czkyaNAg5eTkaOnSpaU2Zl5eniIjI5Wbm6uIiIhSGTOn4KwefX+jvtp93Gufodc10YzVe0o03hM9EjT5s11u7bPuS1b3VnXcnvuxD9L1RcZRZ9u1zWrpyZuaa8Cs9cou+MnZXiMsWK//vp0e+etGl/bw0ECdOnPe/fkGJGvuv/a5jf1ItyYaMHu9zhX+sosGBTiU2CBCG/fnuvSd3i9JkWHBbmPvO56vPq+udakjqmqwmtUO14Z92S5jNIwM0V/TfnAbo1N8lL7KzHFr96Rm1QCdOF3o1h4dFqDjBe7tnkRVlXJOu7fH16yizBM/lmgMb2LCAnSshHXUCgvU0QL37VUa4qOqKDOnZHNp17C6Nn5/skzqeKBTnGZ9lVWivr1a1dbH24+UqG9oUIDOnHN/ne9p10Afph9w26eH/a6px2OxpIICHBrctbFeT80sUf8/90jQ/3h4vie6J2jyypLVMfS6eM1Y7f58CwZ31DVNo13aPt16UA+9t9Gt77R72mrxph9cjn1vggKkxAZR2rg/x9l2bbNaevLG5how28Ma1L+dHnn/4jUoQKd/KtT5wqLjOvQ/d1+lPy/61m27TL4rUU8s3uLWvmxIZ7VqEOlSn6e1pkZYsP7fXVfpkfc2uY1xVf0Ibfq+ZOuYLzzV4U2NsGAtG9JFsdFhJRrb2/tAadTti7Kuw5f37woXoq677jpdddVVCgwM1Ny5cxUSEqIXX3xRv//97zV06FAtXrxYderU0fTp09WrVy+dP39eDz30kD7//HMdOnRIcXFxevTRRzVs2DDnmBeHqMLCQr3yyit68803dejQITVv3lzPPPOM7rrrrhLXWRYh6r5Z60u02JSGvS/3dnvutbuO6XyR3SXQ4ZDDIZfFwVagw+E29vkS7pqBDoc6J8Ro3gMd3O5Lev7TEi0mvjwfgOJdvH40Hr3ca9/LOfZKcw3yRVCAQ7teutmlraRrjTfFrWO+8LWOGmHB2jSuZ4n6ensfKI26fVHWdfjy/l0hz4maO3euYmJitH79ev3pT3/SI488orvvvludOnXSxo0b1bNnTw0YMEAFBQUqLCxUw4YNtWjRIm3fvl3jxo3TU089pYULF3odf+LEiZo3b55mzpypbdu2acSIEbr33nuVmprq9TFnzpxRXl6ey09p2nP01K8WoCTpxY+2uz33xQvdeWNKbfHyNLYvj/0i46gyj+W7tKfuOFLixYQABZSe11b98mnW88u2Fdv3co690lyDfHGu0GhR2n7nbV/WGm+8rWO+sKkju+AnfVmC95bi3gcut25flJc6LqiQIapNmzYaO3asmjVrpjFjxqhKlSqKiYnR4MGD1axZM40bN07Hjx/Xli1bFBwcrOeee07JycmKj49X//799Yc//MFriDpz5oxeeuklzZ49WzfeeKOaNGmiQYMG6d5779Ubb7zhtaaJEycqMjLS+RMbW/zvzX2170RBqY53KWt2/XJQ/drPbWvvcdeDJ/37HP8UAvzGFX1T/mrPMT9WUnbW7v5lXqW51ly8jvnCto6NWdmX7HOp94HLqdsX5aWOCyrkieWJiYnOfwcGBio6OlpXXXWVs61OnZ/P5zly5OfzF1599VXNnj1bWVlZOn36tM6ePau2bdt6HHvXrl0qKCjQDTfc4NJ+9uxZJSUlea1pzJgxevzxx5238/LySjVINapZst9Zl5YuCb+ccPhrP7etxtGuJxa2bRjln0KA37iiJyx3ahKj7w6d8mM1ZaNz0xjnv0tzrbl4HfOFbR3t4mpcss+l3gcup25flJc6LqiQn0QFB7ueOOZwOFzaHA6HpJ/PbZo/f77+/Oc/64EHHtCnn36q9PR0/eEPf9DZs2c9jn3q1M8H+/Lly5Wenu782b59uxYvXuy1ptDQUEVERLj8lKYmtcJ1reU3KWwU/ebMhecO/M/rekGgw6GgAMfFD7XiaWxfHntts1pu387o1qK2apTwJENfng9A8Yp+S2/cbVcW2/dyjr3SXIN8ERTgcPmWni9rjTfe1jFf2NRRIyy4RN/SK+594HLr9kV5qeOCChmifLF27Vp16tRJjz76qJKSkpSQkKDdu3d77d+qVSuFhoYqKytLCQkJLj+l/Ss6X03vl6ROF33r5WJ/ur5piccb2bO5x/ZZ9yV7fO7OCTEubZ0TYrRsSGe3g7ZGWLAWDO7o1l491PMHn7PuS/Y49oLBHd0WyKAAh9rFRrr1nd7P86eEy4Z0casjqmqw2jdy/Z9X54QY3dfR8/bt0vTS/0u7oFa1QJ/aPYkO83xYNo2uWuIxvPGljjrhZfdBtS9zSY4t3f+QFDW4S+MS9+3dus6lO/1HaJDnbdg3uYHHfdrbsVhSQQGOUjn2fanD2/MtGNzRrc3TmiJJ0/q2dTv2vQkKkNrFRrm0+b4GBSjwok0TFODQtL5tPW4Xb+3LhnTWxTytNTXCgjXrvmSPYyQ1LPk65gtPdXhz4dt5JeXtfaA06vZFealDqqDfzmvbtq2mTJnibGvcuLGGDx+u4cOHO9scDoeWLFmirKwsPfPMM1q4cKHi4+P17rvvatq0aYqPj1d6erok92/njR07VjNnztTkyZPVpUsX5ebmau3atYqIiNDAgQNLVGdZfDvvgsxj+fp6z3FlHD6p1TuOKve0+3WiXlu1S19mHHW5dounNunnk8jX7DpaoutEZR7L197j+W7X5fgy46g2ZmW7XXvEU7u3OryNvShtv9buPuZyfRVvfb3xVIe3MW6YvFqZx/PdrhP18Ltp2rDvhMv1dzq99JkO5p1xu05Uq7H/UIGH6zMV7f/+QynaezxfQ95NU4GH60S1e+6fOuHhOlG3TP1CO4+cUvPa4Zre/2rtPZ6vJ+Zv0on/XCdqwSOdnfPqPSX1knVcqLvbK59rf85pt+tEFZ3LGwPbO1/HUYs2u43R4YVPdeQ/14kqWsef3v/GWXPR6yUVneP/DemivcfzNfmT77T7WL7bdaKKbpc3B7YvdmxP4zaOrqb3/7XP474+ctFmrdtzzOUaSAPe/pfSv89xu05U0f2gf8dGztdDkts+VnRbFZ23p33aWx1F225rW9/5HIdyf/Q4hqf91Nsx7sua4Km24sbwxFsdRY9FSc5/p+09UeJj35c1yNvr72u7J97qKI11zBdF62hYI8z5PN9nF3iszxdlWXd5qKPSX+LAlxDVq1cvPfzww1qyZIkcDof69eunyMhIffzxx15DlDFG06ZN0+uvv649e/YoKipK7dq101NPPaVrr/1lISxOWYYoAABQNip1iKooCFEAAFQ8lf46UQAAAP5GiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBAiAIAALBQdn9h9DfuwoXg8/Ly/FwJAAAoqQvv2yX5gy6EqDJy8uRJSVJsbPF/rBIAAJQ/J0+eVGRkZLF9+Nt5ZaSwsFAHDhxQ9erV5XA4Lnu8vLw8xcbGav/+/ZX2b/Exx4qvss9PYo6VQWWfn8QcL4cxRidPnlT9+vUVEFD8WU98ElVGAgIC1LBhw1IfNyIiotIeEBcwx4qvss9PYo6VQWWfn8QcbV3qE6gLOLEcAADAAiEKAADAAiGqgggNDdX48eMVGhrq71LKDHOs+Cr7/CTmWBlU9vlJzPHXwonlAAAAFvgkCgAAwAIhCgAAwAIhCgAAwAIhCgAAwAIhqoJ49dVX1bhxY1WpUkXXXHON1q9f7++SrH3xxRe69dZbVb9+fTkcDi1dutTlfmOMxo0bp3r16qlq1arq0aOHMjIy/FOshYkTJ6p9+/aqXr26ateurT59+mjHjh0ufX788UcNGTJE0dHRCg8P15133qnDhw/7qWLfvf7660pMTHRe5C4lJUUff/yx8/6KPr+Lvfzyy3I4HBo+fLizraLP8dlnn5XD4XD5admypfP+ij6/C3744Qfde++9io6OVtWqVXXVVVcpLS3NeX9FXm8aN27stg0dDoeGDBkiqXJsw/Pnz+uZZ55RfHy8qlatqqZNm+qFF15w+bt2ft2GBuXe/PnzTUhIiJk9e7bZtm2bGTx4sImKijKHDx/2d2lW/vGPf5inn37afPjhh0aSWbJkicv9L7/8somMjDRLly41mzdvNrfddpuJj483p0+f9k/BPrrxxhvNO++8Y7Zu3WrS09PNzTffbOLi4sypU6ecfR5++GETGxtrVq5cadLS0kzHjh1Np06d/Fi1b5YtW2aWL19udu7caXbs2GGeeuopExwcbLZu3WqMqfjzK2r9+vWmcePGJjEx0QwbNszZXtHnOH78eHPllVeagwcPOn+OHj3qvL+iz88YY06cOGEaNWpkBg0aZL7++muzZ88e889//tPs2rXL2acirzdHjhxx2X4rVqwwksyqVauMMZVjG06YMMFER0ebjz76yGRmZppFixaZ8PBwM3XqVGcff25DQlQF0KFDBzNkyBDn7fPnz5v69eubiRMn+rGq0nFxiCosLDR169Y1kyZNcrbl5OSY0NBQ88EHH/ihwst35MgRI8mkpqYaY36eT3BwsFm0aJGzz7///W8jyaxbt85fZV62GjVqmLfffrtSze/kyZOmWbNmZsWKFaZbt27OEFUZ5jh+/HjTpk0bj/dVhvkZY8yoUaNMly5dvN5f2dabYcOGmaZNm5rCwsJKsw179+5t7r//fpe2O+64w/Tv398Y4/9tyK/zyrmzZ8/qm2++UY8ePZxtAQEB6tGjh9atW+fHyspGZmamDh065DLfyMhIXXPNNRV2vrm5uZKkmjVrSpK++eYb/fTTTy5zbNmypeLi4irkHM+fP6/58+crPz9fKSkplWp+Q4YMUe/evV3mIlWebZiRkaH69eurSZMm6t+/v7KysiRVnvktW7ZMycnJuvvuu1W7dm0lJSXprbfect5fmdabs2fP6r333tP9998vh8NRabZhp06dtHLlSu3cuVOStHnzZq1Zs0a9evWS5P9tyB8gLueOHTum8+fPq06dOi7tderU0XfffeenqsrOoUOHJMnjfC/cV5EUFhZq+PDh6ty5s1q3bi3p5zmGhIQoKirKpW9Fm+O3336rlJQU/fjjjwoPD9eSJUvUqlUrpaenV4r5zZ8/Xxs3btSGDRvc7qsM2/Caa67RnDlz1KJFCx08eFDPPfecunbtqq1bt1aK+UnSnj179Prrr+vxxx/XU089pQ0bNuixxx5TSEiIBg4cWKnWm6VLlyonJ0eDBg2SVDn2UUkaPXq08vLy1LJlSwUGBur8+fOaMGGC+vfvL8n/7xmEKKAMDRkyRFu3btWaNWv8XUqpa9GihdLT05Wbm6vFixdr4MCBSk1N9XdZpWL//v0aNmyYVqxYoSpVqvi7nDJx4X/ykpSYmKhrrrlGjRo10sKFC1W1alU/VlZ6CgsLlZycrJdeekmSlJSUpK1bt2rmzJkaOHCgn6srXbNmzVKvXr1Uv359f5dSqhYuXKj3339ff/3rX3XllVcqPT1dw4cPV/369cvFNuTXeeVcTEyMAgMD3b5RcfjwYdWtW9dPVZWdC3OqDPMdOnSoPvroI61atUoNGzZ0ttetW1dnz55VTk6OS/+KNseQkBAlJCTo6quv1sSJE9WmTRtNnTq1Uszvm2++0ZEjR9SuXTsFBQUpKChIqampmjZtmoKCglSnTp0KP8eLRUVFqXnz5tq1a1el2IaSVK9ePbVq1cql7YorrnD+2rKyrDf79u3TZ599pgcffNDZVlm24ciRIzV69Gj993//t6666ioNGDBAI0aM0MSJEyX5fxsSosq5kJAQXX311Vq5cqWzrbCwUCtXrlRKSoofKysb8fHxqlu3rst88/Ly9PXXX1eY+RpjNHToUC1ZskSff/654uPjXe6/+uqrFRwc7DLHHTt2KCsrq8LM0ZPCwkKdOXOmUsyve/fu+vbbb5Wenu78SU5OVv/+/Z3/ruhzvNipU6e0e/du1atXr1JsQ0nq3Lmz2+VFdu7cqUaNGkmqHOuNJL3zzjuqXbu2evfu7WyrLNuwoKBAAQGuUSUwMFCFhYWSysE2LPNT13HZ5s+fb0JDQ82cOXPM9u3bzUMPPWSioqLMoUOH/F2alZMnT5pNmzaZTZs2GUnmf//3f82mTZvMvn37jDE/f101KirK/O1vfzNbtmwx//Vf/1VhvnJsjDGPPPKIiYyMNKtXr3b5+nFBQYGzz8MPP2zi4uLM559/btLS0kxKSopJSUnxY9W+GT16tElNTTWZmZlmy5YtZvTo0cbhcJhPP/3UGFPx5+dJ0W/nGVPx5/jEE0+Y1atXm8zMTLN27VrTo0cPExMTY44cOWKMqfjzM+bny1MEBQWZCRMmmIyMDPP++++bsLAw89577zn7VPT15vz58yYuLs6MGjXK7b7KsA0HDhxoGjRo4LzEwYcffmhiYmLMk08+6ezjz21IiKogpk+fbuLi4kxISIjp0KGD+de//uXvkqytWrXKSHL7GThwoDHm56+sPvPMM6ZOnTomNDTUdO/e3ezYscO/RfvA09wkmXfeecfZ5/Tp0+bRRx81NWrUMGFhYeb22283Bw8e9F/RPrr//vtNo0aNTEhIiKlVq5bp3r27M0AZU/Hn58nFIaqiz7Fv376mXr16JiQkxDRo0MD07dvX5fpJFX1+F/z97383rVu3NqGhoaZly5bmzTffdLm/oq83//znP40kjzVXhm2Yl5dnhg0bZuLi4kyVKlVMkyZNzNNPP23OnDnj7OPPbegwpshlPwEAAFAinBMFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAABggRAFAEWsW7dOgYGBLn/MFQA84c++AEARDz74oMLDwzVr1izt2LFD9evX93dJAMopPokCgP84deqUFixYoEceeUS9e/fWnDlzXO5ftmyZmjVrpipVquj666/X3Llz5XA4lJOT4+yzZs0ade3aVVWrVlVsbKwee+wx5efn/7oTAfCrIEQBwH8sXLhQLVu2VIsWLXTvvfdq9uzZuvBhfWZmpu666y716dNHmzdv1h//+Ec9/fTTLo/fvXu3brrpJt15553asmWLFixYoDVr1mjo0KH+mA6AMsav8wDgPzp37qx77rlHw4YN07lz51SvXj0tWrRI1113nUaPHq3ly5fr22+/dfYfO3asJkyYoOzsbEVFRenBBx9UYGCg3njjDWefNWvWqFu3bsrPz1eVKlX8MS0AZYRPogBA0o4dO7R+/Xr169dPkhQUFKS+fftq1qxZzvvbt2/v8pgOHTq43N68ebPmzJmj8PBw58+NN96owsJCZWZm/joTAfCrCfJ3AQBQHsyaNUvnzp1zOZHcGKPQ0FDNmDGjRGOcOnVKf/zjH/XYY4+53RcXF1dqtQIoHwhRAH7zzp07p3nz5mny5Mnq2bOny319+vTRBx98oBYtWugf//iHy30bNmxwud2uXTtt375dCQkJZV4zAP/jnCgAv3lLly5V3759deTIEUVGRrrcN2rUKH3++edauHChWrRooREjRuiBBx5Qenq6nnjiCX3//ffKyclRZGSktmzZoo4dO+r+++/Xgw8+qGrVqmn79u1asWJFiT/NAlBxcE4UgN+8WbNmqUePHm4BSpLuvPNOpaWl6eTJk1q8eLE+/PBDJSYm6vXXX3d+Oy80NFSSlJiYqNTUVO3cuVNdu3ZVUlKSxo0bx7WmgEqKT6IAwNKECRM0c+ZM7d+/39+lAPADzokCgBJ67bXX1L59e0VHR2vt2rWaNGkS14ACfsMIUQBQQhkZGXrxxRd14sQJxcXF6YknntCYMWP8XRYAP+HXeQAAABY4sRwAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMACIQoAAMDC/wfGGnH4UTTIJwAAAABJRU5ErkJggg==", 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -573,9 +8291,9 @@ } ], "source": [ - "df.plot.scatter(x=\"Age\", y=\"Sex\")\n", + "df.plot.scatter(x=\"High\", y=\"Low\")\n", "\n", - "df.plot.scatter(x=\"Pclass\", y=\"Age\")" + "df.plot.scatter(x=\"Open\", y=\"Low\")" ] }, { @@ -587,22 +8305,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -612,8 +8320,30 @@ } ], "source": [ - "plot = data.groupby([\"Pclass\", \"Survived\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n", - "plot.legend([\"Not survived\", \"Survived\"])" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data = {\n", + " \"Date\": [\"1992-06-26\", \"1992-06-29\", \"1992-06-30\", \"1992-07-01\"],\n", + " \"Open\": [0.328125, 0.339844, 0.367188, 0.351563],\n", + " \"High\": [0.347656, 0.367188, 0.371094, 0.359375],\n", + " \"Low\": [0.320313, 0.332031, 0.343750, 0.339844],\n", + " \"Close\": [0.335938, 0.359375, 0.347656, 0.355469],\n", + " \"Adj Close\": [0.260703, 0.278891, 0.269797, 0.275860],\n", + " \"Volume\": [224358400, 58732800, 34777600, 18316800],\n", + "}\n", + "\n", + "# Создание DataFrame\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Группировка данных и построение графика\n", + "plot = df.groupby([\"Date\", \"Low\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n", + "\n", + "# Настройка легенды\n", + "plot.legend([\"Date\", \"Low\"])\n", + "\n", + "# Показать график\n", + "plt.show()" ] }, { @@ -625,7 +8355,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -675,6 +8405,7 @@ "source": [ "from datetime import datetime\n", "import matplotlib.dates as md\n", + "import pandas as pd\n", "\n", "ts = pd.read_csv(\"data/dollar.csv\")\n", "ts[\"date\"] = ts.apply(lambda row: datetime.strptime(row[\"my_date\"], \"%d.%m.%Y\"), axis=1)\n",