Fix case for StreamingDataFrame class in docs (#540)

Author: stereosky

docs/api-reference/dataframe.md

@@ -56,7 +56,7 @@ usage, see `streamingdataframe.md` under the `docs/` folder.
 ***Example Snippet:***
 
 ```python
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.apply(a_func)
 sdf = sdf.filter(another_func)
 sdf = sdf.to_topic(topic_obj)
@@ -101,7 +101,7 @@ def func(d: dict, state: State):
         state.set("my_store_key") = value
     return {k: v.upper() if isinstance(v, str) else v for k, v in d.items()}
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.apply(func, stateful=True)
 sdf = sdf.apply(lambda d: {k: v for k,v in d.items() if isinstance(v, str)})
 
@@ -166,7 +166,7 @@ def func(values: list, state: State):
         state.set("my_store_key") = value
     values.append("new_item")
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.update(func, stateful=True)
 # does not require reassigning
 sdf.update(lambda v: v.append(1))
@@ -229,7 +229,7 @@ def func(d: dict, state: State):
         return True
     return False
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.filter(func, stateful=True)
 ```
 
@@ -289,7 +289,7 @@ def func(d: dict, state: State):
     d["customer_total"] = new_total
     return d
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.group_by("customer_account_id")
 sdf = sdf.apply(func, stateful=True)
 ```
@@ -335,7 +335,7 @@ Check if the key is present in the Row value.
 # Add new column 'has_column' which contains a boolean indicating
 # the presence of 'column_x'
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf['has_column'] = sdf.contains('column_x')
 ```
 
@@ -859,7 +859,7 @@ original `StreamingDataFrame` is returned for chaining (`sdf.update().print()`).
 # Remove columns "x" and "y" from the value.
 # This would transform {"x": 1, "y": 2, "z": 3} to {"z": 3}
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf.drop(["x", "y"])
 ```
 
@@ -965,7 +965,7 @@ with `StreamingSeries`, and you shouldn't have to think about them explicitly.
 # Random methods for example purposes. More detailed explanations found under
 # various methods or in the docs folder.
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf["column_a"].apply(a_func).apply(diff_func, stateful=True)
 sdf["my_new_bool_field"] = sdf["column_b"].contains("this_string")
 sdf["new_sum_field"] = sdf["column_c"] + sdf["column_d"] + 2
@@ -1038,7 +1038,7 @@ def func(value: str, state: State):
         state.set("my_store_key") = value
     return v.upper()
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf["new_col"] = sdf["a_column"]["nested_dict_key"].apply(func, stateful=True)
 sdf["new_col_2"] = sdf["str_col"].apply(lambda v: int(v)) + sdf["str_col2"] + 2
 ```

docs/api-reference/quixstreams.md

@@ -764,7 +764,7 @@ usage, see `streamingdataframe.md` under the `docs/` folder.
 Example Snippet:
 
 ```python
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.apply(a_func)
 sdf = sdf.filter(another_func)
 sdf = sdf.to_topic(topic_obj)
@@ -805,7 +805,7 @@ def func(d: dict, state: State):
         state.set("my_store_key") = value
     return {k: v.upper() if isinstance(v, str) else v for k, v in d.items()}
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.apply(func, stateful=True)
 sdf = sdf.apply(lambda d: {k: v for k,v in d.items() if isinstance(v, str)})
 
@@ -864,7 +864,7 @@ def func(values: list, state: State):
         state.set("my_store_key") = value
     values.append("new_item")
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.update(func, stateful=True)
 # does not require reassigning
 sdf.update(lambda v: v.append(1))
@@ -919,7 +919,7 @@ def func(d: dict, state: State):
         return True
     return False
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.filter(func, stateful=True)
 ```
 
@@ -973,7 +973,7 @@ def func(d: dict, state: State):
     d["customer_total"] = new_total
     return d
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf.group_by("customer_account_id")
 sdf = sdf.apply(func, stateful=True)
 ```
@@ -1011,7 +1011,7 @@ Example Snippet:
 # Add new column 'has_column' which contains a boolean indicating
 # the presence of 'column_x'
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf['has_column'] = sdf.contains('column_x')
 ```
 
@@ -1463,7 +1463,7 @@ Example Snippet:
 # Remove columns "x" and "y" from the value.
 # This would transform {"x": 1, "y": 2, "z": 3} to {"z": 3}
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf.drop(["x", "y"])
 ```
 
@@ -1557,7 +1557,7 @@ Example Snippet:
 # Random methods for example purposes. More detailed explanations found under
 # various methods or in the docs folder.
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf = sdf["column_a"].apply(a_func).apply(diff_func, stateful=True)
 sdf["my_new_bool_field"] = sdf["column_b"].contains("this_string")
 sdf["new_sum_field"] = sdf["column_c"] + sdf["column_d"] + 2
@@ -1620,7 +1620,7 @@ def func(value: str, state: State):
         state.set("my_store_key") = value
     return v.upper()
 
-sdf = StreamingDataframe()
+sdf = StreamingDataFrame()
 sdf["new_col"] = sdf["a_column"]["nested_dict_key"].apply(func, stateful=True)
 sdf["new_col_2"] = sdf["str_col"].apply(lambda v: int(v)) + sdf["str_col2"] + 2
 ```

quixstreams/dataframe/dataframe.py

@@ -101,7 +101,7 @@ class StreamingDataFrame(BaseStreaming):
     Example Snippet:
 
     ```python
-    sdf = StreamingDataframe()
+    sdf = StreamingDataFrame()
     sdf = sdf.apply(a_func)
     sdf = sdf.filter(another_func)
     sdf = sdf.to_topic(topic_obj)
@@ -197,7 +197,7 @@ def func(d: dict, state: State):
                 state.set("my_store_key") = value
             return {k: v.upper() if isinstance(v, str) else v for k, v in d.items()}
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf = sdf.apply(func, stateful=True)
         sdf = sdf.apply(lambda d: {k: v for k,v in d.items() if isinstance(v, str)})
 
@@ -291,7 +291,7 @@ def func(values: list, state: State):
                 state.set("my_store_key") = value
             values.append("new_item")
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf = sdf.update(func, stateful=True)
         # does not require reassigning
         sdf.update(lambda v: v.append(1))
@@ -379,7 +379,7 @@ def func(d: dict, state: State):
                 return True
             return False
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf = sdf.filter(func, stateful=True)
         ```
 
@@ -469,7 +469,7 @@ def func(d: dict, state: State):
             d["customer_total"] = new_total
             return d
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf = sdf.group_by("customer_account_id")
         sdf = sdf.apply(func, stateful=True)
         ```
@@ -518,7 +518,7 @@ def contains(self, key: str) -> StreamingSeries:
         # Add new column 'has_column' which contains a boolean indicating
         # the presence of 'column_x'
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf['has_column'] = sdf.contains('column_x')
         ```
 
@@ -968,7 +968,7 @@ def drop(
         # Remove columns "x" and "y" from the value.
         # This would transform {"x": 1, "y": 2, "z": 3} to {"z": 3}
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf.drop(["x", "y"])
         ```
 

quixstreams/dataframe/series.py

@@ -108,7 +108,7 @@ class StreamingSeries(BaseStreaming):
     # Random methods for example purposes. More detailed explanations found under
     # various methods or in the docs folder.
 
-    sdf = StreamingDataframe()
+    sdf = StreamingDataFrame()
     sdf = sdf["column_a"].apply(a_func).apply(diff_func, stateful=True)
     sdf["my_new_bool_field"] = sdf["column_b"].contains("this_string")
     sdf["new_sum_field"] = sdf["column_c"] + sdf["column_d"] + 2
@@ -174,7 +174,7 @@ def func(value: str, state: State):
                 state.set("my_store_key") = value
             return v.upper()
 
-        sdf = StreamingDataframe()
+        sdf = StreamingDataFrame()
         sdf["new_col"] = sdf["a_column"]["nested_dict_key"].apply(func, stateful=True)
         sdf["new_col_2"] = sdf["str_col"].apply(lambda v: int(v)) + sdf["str_col2"] + 2
         ```