Metadata-Version: 2.1
Name: psython
Version: 0.0.8
Summary: A package for SPSS methods
Home-page: https://github.com/cxt9/psython
Author: Doron Goldberg
Author-email: doron.goldberg@gmail.com
License: UNKNOWN
Description: # Psython
        
        This package include SPSS related calculations done using python.
        
        ## Installation
        
        ```
        pip install psython
        ```
        
        ## Importing
        
        ```
        import psython as psy
        ```
        
        ## Cronbach's alpha - with "if deleted"
        
        This package is for calculating Cronbach's alpha of an entire dataset with a "if deleted" table for finding items that should be removed.
        
        The package is using the pingouin package for the actuall calculation of Cronbach's alpha.
        
        ### Usage
        
        Here is an example of the SAQ DataFrame (q3r = q3 reversed):
        
        <div>
        
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>q1</th>
              <th>q2</th>
              <th>q4</th>
              <th>q5</th>
              <th>q6</th>
              <th>q7</th>
              <th>q8</th>
              <th>q9</th>
              <th>q10</th>
              <th>q11</th>
              <th>...</th>
              <th>q15</th>
              <th>q16</th>
              <th>q17</th>
              <th>q18</th>
              <th>q19</th>
              <th>q20</th>
              <th>q21</th>
              <th>q22</th>
              <th>q23</th>
              <th>q3r</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>0</th>
              <td>2</td>
              <td>1</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>3</td>
              <td>1</td>
              <td>1</td>
              <td>2</td>
              <td>1</td>
              <td>...</td>
              <td>2</td>
              <td>3</td>
              <td>1</td>
              <td>2</td>
              <td>3</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>5</td>
              <td>2</td>
            </tr>
            <tr>
              <th>1</th>
              <td>1</td>
              <td>1</td>
              <td>3</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>5</td>
              <td>2</td>
              <td>2</td>
              <td>...</td>
              <td>4</td>
              <td>3</td>
              <td>2</td>
              <td>2</td>
              <td>3</td>
              <td>4</td>
              <td>4</td>
              <td>4</td>
              <td>2</td>
              <td>2</td>
            </tr>
            <tr>
              <th>2</th>
              <td>2</td>
              <td>3</td>
              <td>2</td>
              <td>4</td>
              <td>1</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>3</td>
              <td>...</td>
              <td>2</td>
              <td>3</td>
              <td>2</td>
              <td>3</td>
              <td>1</td>
              <td>4</td>
              <td>3</td>
              <td>2</td>
              <td>2</td>
              <td>4</td>
            </tr>
            <tr>
              <th>3</th>
              <td>3</td>
              <td>1</td>
              <td>4</td>
              <td>3</td>
              <td>3</td>
              <td>4</td>
              <td>2</td>
              <td>2</td>
              <td>4</td>
              <td>2</td>
              <td>...</td>
              <td>3</td>
              <td>3</td>
              <td>2</td>
              <td>4</td>
              <td>2</td>
              <td>4</td>
              <td>4</td>
              <td>4</td>
              <td>3</td>
              <td>5</td>
            </tr>
            <tr>
              <th>4</th>
              <td>2</td>
              <td>1</td>
              <td>2</td>
              <td>2</td>
              <td>3</td>
              <td>3</td>
              <td>2</td>
              <td>4</td>
              <td>2</td>
              <td>2</td>
              <td>...</td>
              <td>2</td>
              <td>2</td>
              <td>2</td>
              <td>3</td>
              <td>3</td>
              <td>4</td>
              <td>2</td>
              <td>4</td>
              <td>4</td>
              <td>3</td>
            </tr>
          </tbody>
        </table>
        <p>5 rows × 23 columns</p>
        </div>
        
        ```
        psy.cronbach_alpha_scale_if_deleted(df)
        ```
        
        Where df is the items dataframe (each item as a column) and the function will return two objects - the Cronbach's alpha of the entire DataFrame at position 0 and the table of the "if delete" items in position 1.
        
        <div>
        
        <table border="1" class="dataframe">
          <thead>
            <tr style="text-align: right;">
              <th></th>
              <th>Item</th>
              <th>Scale Mean if Item Deleted</th>
              <th>Scale Variance if Item Deleted</th>
              <th>Corrected Item-Total Correlation</th>
              <th>Cronbach's Alpha if Item Deleted</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <th>0</th>
              <td>q1</td>
              <td>59.892260</td>
              <td>90.121072</td>
              <td>0.520928</td>
              <td>0.791887</td>
            </tr>
            <tr>
              <th>1</th>
              <td>q2</td>
              <td>60.642940</td>
              <td>101.063899</td>
              <td>-0.163158</td>
              <td>0.819978</td>
            </tr>
            <tr>
              <th>2</th>
              <td>q4</td>
              <td>59.480358</td>
              <td>87.967999</td>
              <td>0.569241</td>
              <td>0.788177</td>
            </tr>
            <tr>
              <th>3</th>
              <td>q5</td>
              <td>59.544146</td>
              <td>89.303401</td>
              <td>0.480579</td>
              <td>0.792419</td>
            </tr>
            <tr>
              <th>4</th>
              <td>q6</td>
              <td>60.039284</td>
              <td>87.605071</td>
              <td>0.482416</td>
              <td>0.791397</td>
            </tr>
            <tr>
              <th>5</th>
              <td>q7</td>
              <td>59.342668</td>
              <td>85.655685</td>
              <td>0.594245</td>
              <td>0.785032</td>
            </tr>
            <tr>
              <th>6</th>
              <td>q8</td>
              <td>60.029560</td>
              <td>89.900293</td>
              <td>0.503704</td>
              <td>0.792141</td>
            </tr>
            <tr>
              <th>7</th>
              <td>q9</td>
              <td>59.420070</td>
              <td>100.881838</td>
              <td>-0.137191</td>
              <td>0.828613</td>
            </tr>
            <tr>
              <th>8</th>
              <td>q10</td>
              <td>59.985609</td>
              <td>92.232867</td>
              <td>0.355784</td>
              <td>0.798693</td>
            </tr>
            <tr>
              <th>9</th>
              <td>q11</td>
              <td>60.011280</td>
              <td>88.790145</td>
              <td>0.568318</td>
              <td>0.789112</td>
            </tr>
            <tr>
              <th>10</th>
              <td>q12</td>
              <td>59.106962</td>
              <td>88.451979</td>
              <td>0.562942</td>
              <td>0.788889</td>
            </tr>
            <tr>
              <th>11</th>
              <td>q13</td>
              <td>59.817192</td>
              <td>87.839720</td>
              <td>0.576902</td>
              <td>0.787798</td>
            </tr>
            <tr>
              <th>12</th>
              <td>q14</td>
              <td>59.390121</td>
              <td>87.491716</td>
              <td>0.562476</td>
              <td>0.787931</td>
            </tr>
            <tr>
              <th>13</th>
              <td>q15</td>
              <td>59.500194</td>
              <td>88.766051</td>
              <td>0.484296</td>
              <td>0.791916</td>
            </tr>
            <tr>
              <th>14</th>
              <td>q16</td>
              <td>59.387009</td>
              <td>88.329154</td>
              <td>0.570772</td>
              <td>0.788520</td>
            </tr>
            <tr>
              <th>15</th>
              <td>q17</td>
              <td>59.799689</td>
              <td>88.441961</td>
              <td>0.587849</td>
              <td>0.788165</td>
            </tr>
            <tr>
              <th>16</th>
              <td>q18</td>
              <td>59.697783</td>
              <td>85.993065</td>
              <td>0.608925</td>
              <td>0.784771</td>
            </tr>
            <tr>
              <th>17</th>
              <td>q19</td>
              <td>59.974329</td>
              <td>104.442142</td>
              <td>-0.295795</td>
              <td>0.832243</td>
            </tr>
            <tr>
              <th>18</th>
              <td>q20</td>
              <td>58.642163</td>
              <td>91.699140</td>
              <td>0.313782</td>
              <td>0.800711</td>
            </tr>
            <tr>
              <th>19</th>
              <td>q21</td>
              <td>59.095683</td>
              <td>87.678779</td>
              <td>0.561128</td>
              <td>0.788157</td>
            </tr>
            <tr>
              <th>20</th>
              <td>q22</td>
              <td>59.378452</td>
              <td>101.109248</td>
              <td>-0.152704</td>
              <td>0.823798</td>
            </tr>
            <tr>
              <th>21</th>
              <td>q23</td>
              <td>58.831972</td>
              <td>98.820783</td>
              <td>-0.044039</td>
              <td>0.818680</td>
            </tr>
            <tr>
              <th>22</th>
              <td>q3r</td>
              <td>58.851809</td>
              <td>89.021221</td>
              <td>0.434762</td>
              <td>0.794258</td>
            </tr>
          </tbody>
        </table>
        </div>
        
        ## Reliability: Split-Half
        
        ```
        result = psy.split_half_reliability(df)
        ```
        
        Will return an array of values:
        
        ### pearson
        
        ### spearman_brown
        
        ### a_croncha_1
        
        ### a_croncha_2
        
        ### N1
        
        ### N2
        
        ### table
        
        ```
        print(result.table)
        ```
        
        Will print the output in the familiar SPSS format.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
