stdout

dict_keys(['Datos', 'Funciones', 'Cálculos', 'Histogramas'])


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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>empresa</th>
      <th>sector</th>
      <th>regimen</th>
      <th>empleados</th>
      <th>antigüedad</th>
      <th>facturación</th>
      <th>impuestos</th>
      <th>costes</th>
      <th>ventas</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>2</td>
      <td>36</td>
      <td>5189.92</td>
      <td>1186.55</td>
      <td>1008.35</td>
      <td>Bajo</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>15</td>
      <td>6323.34</td>
      <td>1409.03</td>
      <td>1025.94</td>
      <td>Bajo</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>240</td>
      <td>9326.87</td>
      <td>1779.60</td>
      <td>1194.87</td>
      <td>Alto</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>4</td>
      <td>248</td>
      <td>10126.84</td>
      <td>1951.41</td>
      <td>1255.34</td>
      <td>Muy alto</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Alimentación</td>
      <td>Propiedad</td>
      <td>1</td>
      <td>10</td>
      <td>2032.89</td>
      <td>605.07</td>
      <td>760.57</td>
      <td>Bajo</td>
    </tr>
  </tbody>
</table>
</div>

   empresa        sector    regimen  empleados  antigüedad  facturación  \
0        1  Alimentación   Alquiler          2          36      5189.92   
1        2  Alimentación   Alquiler          3          15      6323.34   
2        3  Alimentación   Alquiler          3         240      9326.87   
3        4  Alimentación   Alquiler          4         248     10126.84   
4        5  Alimentación  Propiedad          1          10      2032.89   

   impuestos   costes    ventas  
0    1186.55  1008.35      Bajo  
1    1409.03  1025.94      Bajo  
2    1779.60  1194.87      Alto  
3    1951.41  1255.34  Muy alto  
4     605.07   760.57      Bajo  

{'Mean': 85.16,
 'Geometric Mean': 51.95375264314718,
 'Mode': 6,
 'Median': 65.0,
 'Sample Standard Deviation': 72.77286582236542,
 'Sample Variance': 5295.89,
 'Quartile 1 (25%)': 20.0,
 'Quartile 2 (50%) - Median': 65.0,
 'Quartile 3 (75%)': 140.0,
 'Percentile 33': 34.879999999999995,
 'Skewness - Coefficient of Asymmetry': 0.8279839130027897,
 'Kurtosis - Coefficient of Curtosis': -0.20967238393584298,
 'Minimum': 6,
 'Maximum': 248,
 'Range': 242,
 'Interquartile Range (IQR)': 120.0,
 'Coefficient of Variation (CV)': 0.8545428114415855}

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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Unnamed: 0</th>
      <th>Ejercicio 1 (d) y (e)</th>
      <th>Unnamed: 2</th>
      <th>Unnamed: 3</th>
      <th>Unnamed: 4</th>
      <th>Unnamed: 5</th>
      <th>Unnamed: 6</th>
      <th>Unnamed: 7</th>
      <th>Unnamed: 8</th>
      <th>1 (d) Cálculo nuevas variables</th>
      <th>Unnamed: 10</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>empresa</td>
      <td>sector</td>
      <td>regimen</td>
      <td>empleados</td>
      <td>antigüedad</td>
      <td>facturación</td>
      <td>impuestos</td>
      <td>costes</td>
      <td>ventas</td>
      <td>Beneficio</td>
      <td>Impuestos_modif</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>2</td>
      <td>36</td>
      <td>5189.92</td>
      <td>1186.55</td>
      <td>1008.35</td>
      <td>Bajo</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>15</td>
      <td>6323.34</td>
      <td>1409.03</td>
      <td>1025.94</td>
      <td>Bajo</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>240</td>
      <td>9326.87</td>
      <td>1779.6</td>
      <td>1194.87</td>
      <td>Alto</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
</div>

  Unnamed: 0 Ejercicio 1 (d) y (e) Unnamed: 2 Unnamed: 3  Unnamed: 4  \
0        NaN                   NaN        NaN        NaN         NaN   
1    empresa                sector    regimen  empleados  antigüedad   
2          1          Alimentación   Alquiler          2          36   
3          2          Alimentación   Alquiler          3          15   
4          3          Alimentación   Alquiler          3         240   

    Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8  \
0          NaN        NaN        NaN        NaN   
1  facturación  impuestos     costes     ventas   
2      5189.92    1186.55    1008.35       Bajo   
3      6323.34    1409.03    1025.94       Bajo   
4      9326.87     1779.6    1194.87       Alto   

  1 (d) Cálculo nuevas variables      Unnamed: 10  
0                            NaN              NaN  
1                      Beneficio  Impuestos_modif  
2                            NaN              NaN  
3                            NaN              NaN  
4                            NaN              NaN  

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      <th>sector</th>
      <th>regimen</th>
      <th>empleados</th>
      <th>antigüedad</th>
      <th>facturación</th>
      <th>impuestos</th>
      <th>costes</th>
      <th>ventas</th>
      <th>Beneficio</th>
      <th>Impuestos_modif</th>
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    <tr>
      <th>0</th>
      <td>1</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>2</td>
      <td>36</td>
      <td>5189.92</td>
      <td>1186.55</td>
      <td>1008.35</td>
      <td>Bajo</td>
      <td>NaN</td>
      <td>NaN</td>
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    <tr>
      <th>1</th>
      <td>2</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>15</td>
      <td>6323.34</td>
      <td>1409.03</td>
      <td>1025.94</td>
      <td>Bajo</td>
      <td>NaN</td>
      <td>NaN</td>
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    <tr>
      <th>2</th>
      <td>3</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>3</td>
      <td>240</td>
      <td>9326.87</td>
      <td>1779.6</td>
      <td>1194.87</td>
      <td>Alto</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Alimentación</td>
      <td>Alquiler</td>
      <td>4</td>
      <td>248</td>
      <td>10126.84</td>
      <td>1951.41</td>
      <td>1255.34</td>
      <td>Muy alto</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Alimentación</td>
      <td>Propiedad</td>
      <td>1</td>
      <td>10</td>
      <td>2032.89</td>
      <td>605.07</td>
      <td>760.57</td>
      <td>Bajo</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
</div>

  empresa        sector    regimen empleados antigüedad facturación impuestos  \
0       1  Alimentación   Alquiler         2         36     5189.92   1186.55   
1       2  Alimentación   Alquiler         3         15     6323.34   1409.03   
2       3  Alimentación   Alquiler         3        240     9326.87    1779.6   
3       4  Alimentación   Alquiler         4        248    10126.84   1951.41   
4       5  Alimentación  Propiedad         1         10     2032.89    605.07   

    costes    ventas  Beneficio  Impuestos_modif  
0  1008.35      Bajo        NaN              NaN  
1  1025.94      Bajo        NaN              NaN  
2  1194.87      Alto        NaN              NaN  
3  1255.34  Muy alto        NaN              NaN  
4   760.57      Bajo        NaN              NaN  

{'empleados': {'Mean': 2.68,
  'Median': 3.0,
  'Mode': 2.0,
  'Sample Standard Deviation': 0.8020806277010643,
  'Sample Variance': 0.6433333333333332,
  'Kurtosis': -0.5242679937185879,
  'Skewness': 0.1440834812060239,
  'Maximum': 4.0,
  'Minimum': 1.0,
  'Range': 3.0,
  'Percentile 25': 2.0,
  'Interquartile Range (IQR)': 1.0,
  'Coefficient of Variation (CV)': 0.29928381630636725},
 'antigüedad': {'Mean': 85.16,
  'Median': 65.0,
  'Mode': 6.0,
  'Sample Standard Deviation': 72.77286582236542,
  'Sample Variance': 5295.89,
  'Kurtosis': -0.20967238393584298,
  'Skewness': 0.8279839130027897,
  'Maximum': 248.0,
  'Minimum': 6.0,
  'Range': 242.0,
  'Percentile 25': 20.0,
  'Interquartile Range (IQR)': 120.0,
  'Coefficient of Variation (CV)': 0.8545428114415855},
 'facturación': {'Mean': 7885.8924,
  'Median': 8626.36,
  'Mode': 2032.89,
  'Sample Standard Deviation': 2413.2486655945754,
  'Sample Variance': 5823769.121994,
  'Kurtosis': -0.03668600215601492,
  'Skewness': -0.8001230784308478,
  'Maximum': 11666.3,
  'Minimum': 2032.89,
  'Range': 9633.41,
  'Percentile 25': 6154.99,
  'Interquartile Range (IQR)': 3304.8999999999996,
  'Coefficient of Variation (CV)': 0.3060209984090799},
 'impuestos': {'Mean': 1615.5940000000003,
  'Median': 1736.4,
  'Mode': 605.07,
  'Sample Standard Deviation': 391.4995507660258,
  'Sample Variance': 153271.89825,
  'Kurtosis': 0.440442912200921,
  'Skewness': -0.9384403286557719,
  'Maximum': 2212.79,
  'Minimum': 605.07,
  'Range': 1607.7199999999998,
  'Percentile 25': 1409.03,
  'Interquartile Range (IQR)': 494.1500000000001,
  'Coefficient of Variation (CV)': 0.24232545476526016},
 'costes': {'Mean': 1167.2708333333333,
  'Median': 1188.82,
  'Mode': 760.57,
  'Sample Standard Deviation': 222.5289552418168,
  'Sample Variance': 49519.135921014495,
  'Kurtosis': 4.219981171484024,
  'Skewness': 1.3315483897120863,
  'Maximum': 1884.72,
  'Minimum': 760.57,
  'Range': 1124.15,
  'Percentile 25': 1025.7,
  'Interquartile Range (IQR)': 204.26749999999993,
  'Coefficient of Variation (CV)': 0.1906403800104804}}

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Vizly: AI-powered data analysis

Exploring Statistics of 'antigüedad' Column in Retail Data

sacame la media aritmetica, geométrica, moda, mediana, desviación tipica muestral, varianza muestral, cuartil 1,2,3, percentil 33, coeficiente de asimetrica, coeficiente de curtosis, minimo, maximo, rango, amplictud intercuartil y coeficiente de variazión de la columna antiguedad

Summary of Statistical Analysis for 'antigüedad' Column

de la hoja cálculos sacame la media, mediana, moda, desviación tipica muestral, varianza muestral, curtosis, asimetria, máximo, minimo, rango, percentil 25, amplictud intercuartil y coeficiente de variación de la columna empleados, antiguedad, facturación, impuestos y costes

Statistical Analysis Summary for Selected Columns

empleados

antigüedad

facturación

impuestos

costes

de la hoja calulos, sacame un historiograma teniendo en cuenta las clases de facturación y su frecuencia

Histogram of Facturación