Data_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import social_interaction\n",
    "#import code_interaction\n",
    "#import buggy_commit\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import platform\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607 576\n",
      "144 194 219\n"
     ]
    }
   ],
   "source": [
    "if platform.system() == 'Darwin' or platform.system() == 'Linux':\n",
    "    _dir = 'Processed_data/'\n",
    "else:\n",
    "    _dir = 'Processed Data\\\\'\n",
    "Files = [join(_dir, f) for f in listdir(_dir) if isfile(join(_dir, f))]\n",
    "print(len(Files))\n",
    "actual_file = 0\n",
    "usable_files = 0\n",
    "size_s = 0\n",
    "size_m = 0\n",
    "size_l = 0\n",
    "result = []\n",
    "for file in Files:\n",
    "    try:\n",
    "        _df = []\n",
    "        actual_file += 1\n",
    "        df = pd.read_pickle(file)\n",
    "        buggy_commit_data_df = df.loc[0,'buggy_commit_data_df']\n",
    "        commit_data_df = df.loc[0,'commit_data_df']\n",
    "        commit_count = df.loc[0,'commit_count']\n",
    "        sg_data_df = df.loc[0,'sg_data_df']\n",
    "        cg_data_df = df.loc[0,'cg_data_df']\n",
    "        if commit_data_df.shape[0] > 30:\n",
    "            size_l += 1\n",
    "        elif commit_data_df.shape[0] > 15:\n",
    "            size_m += 1\n",
    "        elif commit_data_df.shape[0] > 8:\n",
    "            size_s += 1\n",
    "        if commit_data_df.shape[0] < 8:\n",
    "            continue\n",
    "        for i in range(cg_data_df.shape[0]):\n",
    "            buggy_commit_count = buggy_commit_data_df[buggy_commit_data_df['committer'] == cg_data_df.loc[i,'committer']]['count']\n",
    "            if len(buggy_commit_count) == 0:\n",
    "                continue\n",
    "            commit_count = commit_data_df[commit_data_df['committer'] == cg_data_df.loc[i,'committer']]['count']\n",
    "            if len(commit_count) == 0:\n",
    "                continue\n",
    "            node_degree = cg_data_df[cg_data_df['committer'] == cg_data_df.loc[i,'committer']]['count']\n",
    "            #print([buggy_commit_count.values[0],commit_count.values[0],node_degree.values[0]])\n",
    "            _df.append([buggy_commit_count.values[0]/commit_count.values[0],node_degree.values[0]])\n",
    "        df = pd.DataFrame(_df, columns = ['per','degree'])\n",
    "        if(df.shape[0] <= 0):\n",
    "            continue\n",
    "        usable_files += 1\n",
    "        degree_d = np.array(df['degree'].values.tolist())\n",
    "        #first = np.int32(np.percentile(degree_d,99.9))\n",
    "        #second = np.int32(np.percentile(degree_d,99))\n",
    "        #third = np.int32(np.percentile(degree_d,80))\n",
    "        forth = np.int32(np.percentile(degree_d,95))\n",
    "        first_l = []\n",
    "        second_l = []\n",
    "        third_l = []\n",
    "        forth_l = []\n",
    "        for i in range(df.shape[0]):\n",
    "            if df.loc[i,'degree'] < forth:\n",
    "                forth_l.append(df.loc[i,'per'])\n",
    "            #elif df.loc[i,'degree'] < third:\n",
    "            #    third_l.append(df.loc[i,'per'])\n",
    "            #elif df.loc[i,'degree'] < second:\n",
    "            #    second_l.append(df.loc[i,'per'])\n",
    "            else:\n",
    "                first_l.append(df.loc[i,'per'])\n",
    "        #print(round(np.median(first_l),2),round(np.median(second_l),2),round(np.median(third_l),2),round(np.median(forth_l),2))\n",
    "        result.append([file,commit_data_df.shape[0],round(np.median(first_l),2),round(np.median(forth_l),2)])\n",
    "    except:\n",
    "        continue\n",
    "print(actual_file,usable_files)\n",
    "print(size_s,size_m,size_l)\n",
    "c_result_df = pd.DataFrame(result, columns = ['project','c_size','c_experienced','c_inexperienced'])\n",
    "c_result_df_n = c_result_df.set_index('project')\n",
    "c_result_df.to_excel('new_results_1/Result_data_team_c.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "607 564\n",
      "144 194 219\n"
     ]
    }
   ],
   "source": [
    "if platform.system() == 'Darwin' or platform.system() == 'Linux':\n",
    "    _dir = 'Processed_data/'\n",
    "else:\n",
    "    _dir = 'Processed Data\\\\'\n",
    "Files = [join(_dir, f) for f in listdir(_dir) if isfile(join(_dir, f))]\n",
    "actual_file = 0\n",
    "usable_files = 0\n",
    "size_s = 0\n",
    "size_m = 0\n",
    "size_l = 0\n",
    "result = []\n",
    "for file in Files:\n",
    "    try:\n",
    "        _df = []\n",
    "        actual_file += 1\n",
    "        df = pd.read_pickle(file)\n",
    "        buggy_commit_data_df = df.loc[0,'buggy_commit_data_df']\n",
    "        commit_data_df = df.loc[0,'commit_data_df']\n",
    "        commit_count = df.loc[0,'commit_count']\n",
    "        sg_data_df = df.loc[0,'sg_data_df']\n",
    "        cg_data_df = df.loc[0,'cg_data_df']\n",
    "        if commit_data_df.shape[0] > 30:\n",
    "            size_l += 1\n",
    "        elif commit_data_df.shape[0] > 15:\n",
    "            size_m += 1\n",
    "        elif commit_data_df.shape[0] > 8:\n",
    "            size_s += 1\n",
    "        if commit_data_df.shape[0] < 8:\n",
    "            continue\n",
    "        for i in range(sg_data_df.shape[0]):\n",
    "            buggy_commit_count = buggy_commit_data_df[buggy_commit_data_df['committer'] == sg_data_df.loc[i,'committer']]['count']\n",
    "            if len(buggy_commit_count) == 0:\n",
    "                continue\n",
    "            commit_count = commit_data_df[commit_data_df['committer'] == sg_data_df.loc[i,'committer']]['count']\n",
    "            if len(commit_count) == 0:\n",
    "                continue\n",
    "            node_degree = sg_data_df[sg_data_df['committer'] == sg_data_df.loc[i,'committer']]['count']\n",
    "            #print([buggy_commit_count.values[0],commit_count.values[0],node_degree.values[0]])\n",
    "            _df.append([buggy_commit_count.values[0]/commit_count.values[0],node_degree.values[0]])\n",
    "        df = pd.DataFrame(_df, columns = ['per','degree'])\n",
    "        if(df.shape[0] <= 0):\n",
    "            continue\n",
    "        usable_files += 1\n",
    "        degree_d = np.array(df['degree'].values.tolist())\n",
    "        #first = np.int32(np.percentile(degree_d,99.9))\n",
    "        #second = np.int32(np.percentile(degree_d,99))\n",
    "        #third = np.int32(np.percentile(degree_d,80))\n",
    "        forth = np.int32(np.percentile(degree_d,95))\n",
    "        first_l = []\n",
    "        second_l = []\n",
    "        third_l = []\n",
    "        forth_l = []\n",
    "        for i in range(df.shape[0]):\n",
    "            if df.loc[i,'degree'] < forth:\n",
    "                forth_l.append(df.loc[i,'per'])\n",
    "            #elif df.loc[i,'degree'] < third:\n",
    "            #    third_l.append(df.loc[i,'per'])\n",
    "            #elif df.loc[i,'degree'] < second:\n",
    "            #    second_l.append(df.loc[i,'per'])\n",
    "            else:\n",
    "                first_l.append(df.loc[i,'per'])\n",
    "        #print(round(np.median(first_l),2),round(np.median(second_l),2),round(np.median(third_l),2),round(np.median(forth_l),2))\n",
    "        result.append([file,commit_data_df.shape[0],round(np.median(first_l),2),round(np.median(forth_l),2)])\n",
    "    except KeyError as e:\n",
    "        print(e)\n",
    "        continue\n",
    "print(actual_file,usable_files)\n",
    "print(size_s,size_m,size_l)\n",
    "s_result_df = pd.DataFrame(result, columns = ['project','s_size','s_experienced','s_inexperienced'])\n",
    "s_result_df_n = s_result_df.set_index('project')\n",
    "s_result_df_n.to_excel('new_results_1/Result_data_team_s.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_df = pd.concat([c_result_df_n,s_result_df_n],join='inner',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_df.to_csv('new_results_1/Result_data_team_joined_95.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(564, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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