Unified indent to 4 spaces per level

even-more-python-for-beginners-data-tools/03 - Intro to Pandas/03 - Pandas Series and DataFrame.ipynb

@@ -56,15 +56,17 @@
     }
    ],
    "source": [
-    "airports = pd.Series([\n",
-    "                      'Seattle-Tacoma', \n",
-    "                      'Dulles', \n",
-    "                      'London Heathrow', \n",
-    "                      'Schiphol', \n",
-    "                      'Changi', \n",
-    "                      'Pearson', \n",
-    "                      'Narita'\n",
-    "                      ])\n",
+    "airports = pd.Series(\n",
+    "    [\n",
+    "        'Seattle-Tacoma', \n",
+    "        'Dulles', \n",
+    "        'London Heathrow', \n",
+    "        'Schiphol', \n",
+    "        'Changi', \n",
+    "        'Pearson', \n",
+    "        'Narita'\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "# When using a notebook, you can use the print statement\n",
     "# print(airports) to examine the contents of a variable\n",
@@ -235,15 +237,17 @@
     }
    ],
    "source": [
-    "airports = pd.DataFrame([\n",
-    "                        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
-    "                        ['Dulles', 'Washington', 'USA'],\n",
-    "                        ['London Heathrow', 'London', 'United Kingdom'],\n",
-    "                        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
-    "                        ['Changi', 'Singapore', 'Singapore'],\n",
-    "                        ['Pearson', 'Toronto', 'Canada'],\n",
-    "                        ['Narita', 'Tokyo', 'Japan']\n",
-    "                        ])\n",
+    "airports = pd.DataFrame(\n",
+    "    [\n",
+    "        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
+    "        ['Dulles', 'Washington', 'USA'],\n",
+    "        ['London Heathrow', 'London', 'United Kingdom'],\n",
+    "        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
+    "        ['Changi', 'Singapore', 'Singapore'],\n",
+    "        ['Pearson', 'Toronto', 'Canada'],\n",
+    "        ['Narita', 'Tokyo', 'Japan']\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "airports"
    ]
@@ -350,17 +354,18 @@
     }
    ],
    "source": [
-    "airports = pd.DataFrame([\n",
-    "                        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
-    "                        ['Dulles', 'Washington', 'USA'],\n",
-    "                        ['London Heathrow', 'London', 'United Kingdom'],\n",
-    "                        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
-    "                        ['Changi', 'Singapore', 'Singapore'],\n",
-    "                        ['Pearson', 'Toronto', 'Canada'],\n",
-    "                        ['Narita', 'Tokyo', 'Japan']\n",
-    "                        ],\n",
-    "                        columns = ['Name', 'City', 'Country']\n",
-    "                        )\n",
+    "airports = pd.DataFrame(\n",
+    "    [\n",
+    "        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
+    "        ['Dulles', 'Washington', 'USA'],\n",
+    "        ['London Heathrow', 'London', 'United Kingdom'],\n",
+    "        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
+    "        ['Changi', 'Singapore', 'Singapore'],\n",
+    "        ['Pearson', 'Toronto', 'Canada'],\n",
+    "        ['Narita', 'Tokyo', 'Japan']\n",
+    "    ],\n",
+    "    columns = ['Name', 'City', 'Country']\n",
+    ")\n",
     "\n",
     "airports "
    ]

even-more-python-for-beginners-data-tools/04 - Examining Pandas DataFrame contents/04 - Exploring pandas DataFrame contents.ipynb

@@ -114,17 +114,18 @@
     }
    ],
    "source": [
-    "airports = pd.DataFrame([\n",
-    "                        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
-    "                        ['Dulles', 'Washington', 'USA'],\n",
-    "                        ['Heathrow', 'London', 'United Kingdom'],\n",
-    "                        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
-    "                        ['Changi', 'Singapore', 'Singapore'],\n",
-    "                        ['Pearson', 'Toronto', 'Canada'],\n",
-    "                        ['Narita', 'Tokyo', 'Japan']\n",
-    "                        ],\n",
-    "                        columns = ['Name', 'City', 'Country']\n",
-    "                        )\n",
+    "airports = pd.DataFrame(\n",
+    "    [\n",
+    "        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
+    "        ['Dulles', 'Washington', 'USA'],\n",
+    "        ['Heathrow', 'London', 'United Kingdom'],\n",
+    "        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
+    "        ['Changi', 'Singapore', 'Singapore'],\n",
+    "        ['Pearson', 'Toronto', 'Canada'],\n",
+    "        ['Narita', 'Tokyo', 'Japan']\n",
+    "    ],\n",
+    "    columns = ['Name', 'City', 'Country']\n",
+    ")\n",
     "\n",
     "airports "
    ]

even-more-python-for-beginners-data-tools/05 - Query a pandas Dataframe/05 - Querying DataFrames.ipynb

@@ -115,17 +115,18 @@
     }
    ],
    "source": [
-    "airports = pd.DataFrame([\n",
-    "                        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
-    "                        ['Dulles', 'Washington', 'USA'],\n",
-    "                        ['London Heathrow', 'London', 'United Kingdom'],\n",
-    "                        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
-    "                        ['Changi', 'Singapore', 'Singapore'],\n",
-    "                        ['Pearson', 'Toronto', 'Canada'],\n",
-    "                        ['Narita', 'Tokyo', 'Japan']\n",
-    "                        ],\n",
-    "                        columns = ['Name', 'City', 'Country']\n",
-    "                        )\n",
+    "airports = pd.DataFrame(\n",
+    "    [\n",
+    "        ['Seatte-Tacoma', 'Seattle', 'USA'],\n",
+    "        ['Dulles', 'Washington', 'USA'],\n",
+    "        ['London Heathrow', 'London', 'United Kingdom'],\n",
+    "        ['Schiphol', 'Amsterdam', 'Netherlands'],\n",
+    "        ['Changi', 'Singapore', 'Singapore'],\n",
+    "        ['Pearson', 'Toronto', 'Canada'],\n",
+    "        ['Narita', 'Tokyo', 'Japan']\n",
+    "    ],\n",
+    "    columns = ['Name', 'City', 'Country']\n",
+    ")\n",
     "airports "
    ]
   },

even-more-python-for-beginners-data-tools/07 - Read and write CSV files from pandas DataFrames/07 - Read write CSV files.ipynb

@@ -289,9 +289,9 @@
    ],
    "source": [
     "airports_df = pd.read_csv(\n",
-    "                          'Data/airportsInvalidRows.csv', \n",
-    "                           error_bad_lines=False\n",
-    "                           )\n",
+    "    'Data/airportsInvalidRows.csv', \n",
+    "    error_bad_lines=False\n",
+    ")\n",
     "airports_df"
    ]
   },
@@ -508,9 +508,9 @@
    ],
    "source": [
     "airports_df = pd.read_csv(\n",
-    "                          'Data/airportsNoHeaderRows.csv', \n",
-    "                           header=None\n",
-    "                           )\n",
+    "    'Data/airportsNoHeaderRows.csv', \n",
+    "    header=None\n",
+    ")\n",
     "airports_df"
    ]
   },
@@ -617,10 +617,10 @@
    ],
    "source": [
     "airports_df = pd.read_csv(\n",
-    "                          'Data/airportsNoHeaderRows.csv', \n",
-    "                          header=None, \n",
-    "                          names=['Name', 'City', 'Country']\n",
-    "                          )\n",
+    "    'Data/airportsNoHeaderRows.csv', \n",
+    "    header=None, \n",
+    "    names=['Name', 'City', 'Country']\n",
+    ")\n",
     "airports_df"
    ]
   },
@@ -873,9 +873,9 @@
    "outputs": [],
    "source": [
     "airports_df.to_csv(\n",
-    "                   'Data/MyNewCSVFileNoIndex.csv', \n",
-    "                    index=False\n",
-    "                    )"
+    "    'Data/MyNewCSVFileNoIndex.csv', \n",
+    "    index=False\n",
+    ")"
    ]
   }
  ],
@@ -900,4 +900,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

even-more-python-for-beginners-data-tools/08 - Removing and splitting DataFrame columns/08 - Removing columns.ipynb

@@ -576,7 +576,10 @@
     "# Remove the column ARR_TIME from the DataFrame delays_df\n",
     "\n",
     "#delays_df = delays_df.drop(['ARR_TIME'],axis=1)\n",
-    "delays_df.drop(columns=['ARR_TIME'], inplace=True)\n",
+    "delays_df.drop(\n",
+    "    columns=['ARR_TIME'], \n",
+    "    inplace=True\n",
+    ")\n",
     "delays_df.head()"
    ]
   },
@@ -631,4 +634,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

even-more-python-for-beginners-data-tools/10 - Splitting test and training data with scikit-learn/10 - Train Test split.ipynb

@@ -250,11 +250,11 @@
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "                                                    X, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.3, \n",
-    "                                                    random_state=42\n",
-    "                                                   )"
+    "    X, \n",
+    "    y, \n",
+    "    test_size=0.3, \n",
+    "    random_state=42\n",
+    ")"
    ]
   },
   {
@@ -575,4 +575,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

even-more-python-for-beginners-data-tools/11 - Train a linear regression model with scikit-learn/11 - Train a basic model.ipynb

@@ -47,11 +47,11 @@
     "\n",
     "# Split our data into test and training DataFrames\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "                                                    X, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.3, \n",
-    "                                                    random_state=42\n",
-    "                                                   )"
+    "    X, \n",
+    "    y, \n",
+    "    test_size=0.3, \n",
+    "    random_state=42\n",
+    ")"
    ]
   },
   {

even-more-python-for-beginners-data-tools/12 - Testing a model/12 - Test a model.ipynb

@@ -57,11 +57,11 @@
     "\n",
     "# Split our data into test and training DataFrames\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "                                                    X, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.3, \n",
-    "                                                    random_state=42\n",
-    "                                                   )\n",
+    "    X, \n",
+    "    y, \n",
+    "    test_size=0.3, \n",
+    "    random_state=42\n",
+    ")\n",
     "regressor = LinearRegression()     # Create a scikit learn LinearRegression object\n",
     "regressor.fit(X_train, y_train)    # Use the fit method to train the model using your training data"
    ]

even-more-python-for-beginners-data-tools/13 - Evaluating accuracy of a model using calculations/13 - Evaluate accuracy.ipynb

@@ -48,11 +48,11 @@
     "\n",
     "# Split our data into test and training DataFrames\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "                                                    X, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.3, \n",
-    "                                                    random_state=42\n",
-    "                                                   )\n",
+    "    X, \n",
+    "    y, \n",
+    "    test_size=0.3, \n",
+    "    random_state=42\n",
+    ")\n",
     "regressor = LinearRegression()     # Create a scikit learn LinearRegression object\n",
     "regressor.fit(X_train, y_train)    # Use the fit method to train the model using your training data\n",
     "\n",

even-more-python-for-beginners-data-tools/14 - NumPy vs Pandas/14 - Working with numpy and pandas.ipynb

@@ -47,10 +47,12 @@
     "y = delays_df.loc[:,['ARR_DELAY']] \n",
     "\n",
     "# Split our data into test and training DataFrames\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
-    "                                                    y, \n",
-    "                                                    test_size=0.3, \n",
-    "                                                    random_state=42)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, \n",
+    "    y, \n",
+    "    test_size=0.3, \n",
+    "    random_state=42\n",
+    ")\n",
     "regressor = LinearRegression()     # Create a scikit learn LinearRegression object\n",
     "regressor.fit(X_train, y_train)    # Use the fit method to train the model using your training data\n",
     "\n",
@@ -550,10 +552,13 @@
     }
    ],
    "source": [
-    "airports_array = np.array([\n",
-    "  ['YYZ','Pearson'],\n",
-    "  ['SIN','Changi'],\n",
-    "  ['NRT','Narita']])\n",
+    "airports_array = np.array(\n",
+    "    [\n",
+    "        ['YYZ','Pearson'],\n",
+    "        ['SIN','Changi'],\n",
+    "        ['NRT','Narita']\n",
+    "    ]\n",
+    ")\n",
     "print(airports_array)\n",
     "print(airports_array[0,0])"
    ]