putting examples on trajectories module

Author: joaocarvoli

pymove/utils/trajectories.py

@@ -75,12 +75,12 @@ def read_csv(
     >>> from pymove.utils.trajectories import read_csv
     >>> move_df = read_csv('geolife_sample.csv')
     >>> move_df.head()
-                  lat          lon              datetime  id
-        0   39.984094   116.319236   2008-10-23 05:53:05   1
-        1   39.984198   116.319322	 2008-10-23 05:53:06   1
-        2   39.984224   116.319402	 2008-10-23 05:53:11   1
-        3   39.984211   116.319389	 2008-10-23 05:53:16   1
-        4   39.984217   116.319422	 2008-10-23 05:53:21   1
+              lat          lon              datetime  id
+    0   39.984094   116.319236   2008-10-23 05:53:05   1
+    1   39.984198   116.319322   2008-10-23 05:53:06   1
+    2   39.984224   116.319402   2008-10-23 05:53:11   1
+    3   39.984211   116.319389   2008-10-23 05:53:16   1
+    4   39.984217   116.319422   2008-10-23 05:53:21   1
     """
     data = _read_csv(
         filepath_or_buffer,
@@ -144,8 +144,8 @@ def flatten_dict(
     --------
     >>> from pymove.utils.trajectories import flatten_dict
     >>> d = {'a': 1, 'b': {'c': 2, 'd': 3}}
-    >>> flatten_dict(traj_dict, 'x')
-    {'x_a': 1, 'x_b_c': 2, 'x_b_d': 3}
+    >>> d
+    {'a': 1, 'b': {'c': 2, 'd': 3}}
     """
     if not isinstance(d, dict):
         return {parent_key: d}
@@ -183,26 +183,25 @@ def flatten_columns(data: DataFrame, columns: List) -> DataFrame:
     --------
     >>> from pymove.utils.trajectories import flatten_columns
     >>> move_df
-                  lat          lon              datetime  id   dict_column
-        0   39.984094   116.319236   2008-10-23 05:53:05   1      {'a': 1}
-        1   39.984198   116.319322	 2008-10-23 05:53:06   1      {'b': 2}
-        2   39.984224   116.319402	 2008-10-23 05:53:11   1      {'c': 3}
-        3   39.984211   116.319389	 2008-10-23 05:53:16   1      {'d': 4}
-        4   39.984217   116.319422	 2008-10-23 05:53:21   1      {'e': 5}
-    >>> flatten_columns(moveDf, columns = 'dict_column')
-                  lat          lon             datetime   id
-        0   39.984094   116.319236  2008-10-23 05:53:05    1
-        1   39.984198   116.319322  2008-10-23 05:53:06    1
-        2   39.984224   116.319402  2008-10-23 05:53:11    1
-        3   39.984211   116.319389  2008-10-23 05:53:16    1
-        4   39.984217   116.319422  2008-10-23 05:53:21    1
-
-            dict_column_b   dict_column_d   dict_column_e   dict_column_a   dict_column_c
-        0             NaN             NaN             NaN             1.0             NaN
-        1             2.0             NaN             NaN             NaN             NaN
-        2             NaN             NaN             NaN             NaN             3.0
-        3             NaN             4.0             NaN             NaN             NaN
-        4             NaN             NaN             5.0             NaN             NaN
+              lat          lon              datetime  id           dict_column
+    0   39.984094   116.319236   2008-10-23 05:53:05   1              {'a': 1}
+    1   39.984198   116.319322   2008-10-23 05:53:06   1              {'b': 2}
+    2   39.984224   116.319402   2008-10-23 05:53:11   1      {'c': 3, 'a': 4}
+    3   39.984211   116.319389   2008-10-23 05:53:16   1              {'b': 2}
+    4   39.984217   116.319422   2008-10-23 05:53:21   1      {'a': 3, 'c': 2}
+    >>> flatten_columns(move_df, columns='dict_column')
+              lat            lon               datetime   id
+    dict_column_b         dict_column_c   dict_column_a
+    0   39.984094      116.319236   2008-10-23 05:53:05    1\
+              NaN                   NaN             1.0
+    1   39.984198      116.319322   2008-10-23 05:53:06    1\
+              2.0                   NaN             NaN
+    2   39.984224      116.319402   2008-10-23 05:53:11    1\
+              NaN                   3.0             4.0
+    3   39.984211      116.319389   2008-10-23 05:53:16    1\
+              2.0                   NaN             NaN
+    4   39.984217      116.319422   2008-10-23 05:53:21    1\
+              NaN                   2.0             3.0
     """
     data = data.copy()
     if not isinstance(columns, list):
@@ -370,17 +369,17 @@ def column_to_array(data: DataFrame, column: Text):
     >>> move_df
                   lat          lon              datetime  id   list_column
         0   39.984094   116.319236   2008-10-23 05:53:05   1         [1,2]
-        1   39.984198   116.319322	 2008-10-23 05:53:06   1         [3,4]
-        2   39.984224   116.319402	 2008-10-23 05:53:11   1         [5,6]
-        3   39.984211   116.319389	 2008-10-23 05:53:16   1         [7,8]
-        4   39.984217   116.319422	 2008-10-23 05:53:21   1         [9,10]
+        1   39.984198   116.319322   2008-10-23 05:53:06   1         [3,4]
+        2   39.984224   116.319402   2008-10-23 05:53:11   1         [5,6]
+        3   39.984211   116.319389   2008-10-23 05:53:16   1         [7,8]
+        4   39.984217   116.319422   2008-10-23 05:53:21   1         [9,10]
     >>> column_to_array(moveDf, column = 'list_column')
                   lat          lon              datetime  id   list_column
         0   39.984094   116.319236   2008-10-23 05:53:05   1     [1.0,2.0]
-        1   39.984198   116.319322	 2008-10-23 05:53:06   1     [3.0,4.0]
-        2   39.984224   116.319402	 2008-10-23 05:53:11   1     [5.0,6.0]
-        3   39.984211   116.319389	 2008-10-23 05:53:16   1     [7.0,8.0]
-        4   39.984217   116.319422	 2008-10-23 05:53:21   1     [9.0,10.0]
+        1   39.984198   116.319322   2008-10-23 05:53:06   1     [3.0,4.0]
+        2   39.984224   116.319402   2008-10-23 05:53:11   1     [5.0,6.0]
+        3   39.984211   116.319389   2008-10-23 05:53:16   1     [7.0,8.0]
+        4   39.984217   116.319422   2008-10-23 05:53:21   1     [9.0,10.0]
     """
     data = data.copy()
     if column not in data: