Fix broken build

README.md

@@ -37,6 +37,8 @@ The DataWig API expects your data as a [pandas DataFrame](https://pandas.pydata.
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+### Quickstart Example
+
 For most use cases, the `SimpleImputer` class is the best starting point. For convenience there is the function [SimpleImputer.complete](https://datawig.readthedocs.io/en/latest/source/API.html#datawig.simple_imputer.SimpleImputer.complete) that takes a DataFrame and fits an imputation model for each column with missing values, with all other columns as inputs:
 
 ```python
@@ -60,8 +62,10 @@ You can also impute values in specific columns only (called `output_column` belo
 import datawig
 
 df = datawig.utils.generate_df_string( num_samples=200, 
-                                       data_column_name='sentences', label_column_name='label')
+                                       data_column_name='sentences', 
+                                       label_column_name='label')
 
+df_train, df_test = datawig.utils.random_split(df)
 
 #Initialize a SimpleImputer model
 imputer = datawig.SimpleImputer(
@@ -138,4 +142,4 @@ Run tests:
 ```
 ./venv/bin/pip install -r requirements/requirements.dev.txt
 ./venv/bin/python -m pytest
-```
+```

datawig/imputer.py

@@ -819,14 +819,14 @@ def predict(self,
         predictions = self.predict_above_precision(data_frame, precision_threshold).items()
         for label, imputations in predictions:
             imputation_col = label + imputation_suffix
-            if data_frame.columns.contains(imputation_col):
+            if imputation_col in data_frame.columns:
                 raise ColumnOverwriteException(
                     "DataFrame contains column {}; remove column and try again".format(
                         imputation_col))
 
             if label not in numerical_outputs:
                 imputation_proba_col = label + score_suffix
-                if data_frame.columns.contains(imputation_proba_col):
+                if imputation_proba_col in data_frame.columns:
                     raise ColumnOverwriteException(
                         "DataFrame contains column {}; remove column and try again".format(
                             imputation_proba_col))

requirements/requirements.txt

@@ -1,5 +1,5 @@
-numpy>=1.15.0
-scikit-learn[alldeps]>=0.20.0
-typing>=3.6.6
-pandas>=0.22.0
-mxnet>=1.3.0
+numpy==1.18.0
+scikit-learn[alldeps]==0.22.1
+typing==3.6.6
+pandas==0.25.0
+mxnet==1.4.0