Merge pull request #52447 from mpalumbo7/patch-1

Fixed formatting of documentation

Author: ktoliver

articles/machine-learning/team-data-science-process/explore-data-blob.md

@@ -29,30 +29,30 @@ To explore and manipulate a dataset, it must first be downloaded from the blob s
 
 1. Download the data from Azure blob with the following Python code sample using Blob service. Replace the variable in the following code with your specific values:
 
-```python
-from azure.storage.blob import BlockBlobService
-import tables
-
-STORAGEACCOUNTNAME= <storage_account_name>
-STORAGEACCOUNTKEY= <storage_account_key>
-LOCALFILENAME= <local_file_name>
-CONTAINERNAME= <container_name>
-BLOBNAME= <blob_name>
-
-#download from blob
-t1=time.time()
-blob_service=BlockBlobService(account_name=STORAGEACCOUNTNAME,account_key=STORAGEACCOUNTKEY)
-blob_service.get_blob_to_path(CONTAINERNAME,BLOBNAME,LOCALFILENAME)
-t2=time.time()
-print(("It takes %s seconds to download "+blobname) % (t2 - t1))
-```
+    ```python
+    from azure.storage.blob import BlockBlobService
+    import tables
+
+    STORAGEACCOUNTNAME= <storage_account_name>
+    STORAGEACCOUNTKEY= <storage_account_key>
+    LOCALFILENAME= <local_file_name>
+    CONTAINERNAME= <container_name>
+    BLOBNAME= <blob_name>
+
+    #download from blob
+    t1=time.time()
+    blob_service=BlockBlobService(account_name=STORAGEACCOUNTNAME,account_key=STORAGEACCOUNTKEY)
+    blob_service.get_blob_to_path(CONTAINERNAME,BLOBNAME,LOCALFILENAME)
+    t2=time.time()
+    print(("It takes %s seconds to download "+blobname) % (t2 - t1))
+    ```
 
 1. Read the data into a pandas DataFrame from the downloaded file.
 
-```python
-# LOCALFILE is the file path
-dataframe_blobdata = pd.read_csv(LOCALFILE)
-```
+    ```python
+    # LOCALFILE is the file path
+    dataframe_blobdata = pd.read_csv(LOCALFILENAME)
+    ```
 
 Now you are ready to explore the data and generate features on this dataset.
 
@@ -61,72 +61,72 @@ Here are a few examples of ways to explore data using pandas:
 
 1. Inspect the **number of rows and columns**
 
-```python
-print 'the size of the data is: %d rows and  %d columns' % dataframe_blobdata.shape
-```
+    ```python
+    print 'the size of the data is: %d rows and  %d columns' % dataframe_blobdata.shape
+    ```
 
 1. **Inspect** the first or last few **rows** in the following dataset:
 
-```python
-dataframe_blobdata.head(10)
+    ```python
+    dataframe_blobdata.head(10)
 
-dataframe_blobdata.tail(10)
-```
+    dataframe_blobdata.tail(10)
+    ```
 
 1. Check the **data type** each column was imported as using the following sample code
 
-```python
-for col in dataframe_blobdata.columns:
-    print dataframe_blobdata[col].name, ':\t', dataframe_blobdata[col].dtype
-```
+    ```python
+    for col in dataframe_blobdata.columns:
+        print dataframe_blobdata[col].name, ':\t', dataframe_blobdata[col].dtype
+    ```
 
 1. Check the **basic stats** for the columns in the data set as follows
 
-```python
-dataframe_blobdata.describe()
-```
+    ```python
+    dataframe_blobdata.describe()
+    ```
 
 1. Look at the number of entries for each column value as follows
 
-```python
-dataframe_blobdata['<column_name>'].value_counts()
-```
+    ```python
+    dataframe_blobdata['<column_name>'].value_counts()
+    ```
 
 1. **Count missing values** versus the actual number of entries in each column using the following sample code
 
-```python
-miss_num = dataframe_blobdata.shape[0] - dataframe_blobdata.count()
-print miss_num
-```
+    ```python
+    miss_num = dataframe_blobdata.shape[0] - dataframe_blobdata.count()
+    print miss_num
+    ```
 
 1. If you have **missing values** for a specific column in the data, you can drop them as follows:
 
-```python
-dataframe_blobdata_noNA = dataframe_blobdata.dropna()
-dataframe_blobdata_noNA.shape
-```
+    ```python
+    dataframe_blobdata_noNA = dataframe_blobdata.dropna()
+    dataframe_blobdata_noNA.shape
+    ```
 
-Another way to replace missing values is with the mode function:
+    Another way to replace missing values is with the mode function:
 
-```python
-dataframe_blobdata_mode = dataframe_blobdata.fillna(
-    {'<column_name>': dataframe_blobdata['<column_name>'].mode()[0]})
-```
+    ```python
+    dataframe_blobdata_mode = dataframe_blobdata.fillna(
+        {'<column_name>': dataframe_blobdata['<column_name>'].mode()[0]})
+    ```
 
 1. Create a **histogram** plot using variable number of bins to plot the distribution of a variable
 
-```python
-dataframe_blobdata['<column_name>'].value_counts().plot(kind='bar')
+    ```python
+    dataframe_blobdata['<column_name>'].value_counts().plot(kind='bar')
 
-np.log(dataframe_blobdata['<column_name>']+1).hist(bins=50)
-```
+    np.log(dataframe_blobdata['<column_name>']+1).hist(bins=50)
+    ```
 
 1. Look at **correlations** between variables using a scatterplot or using the built-in correlation function
 
-```python
-# relationship between column_a and column_b using scatter plot
-plt.scatter(dataframe_blobdata['<column_a>'], dataframe_blobdata['<column_b>'])
+    ```python
+    # relationship between column_a and column_b using scatter plot
+    plt.scatter(dataframe_blobdata['<column_a>'], dataframe_blobdata['<column_b>'])
 
-# correlation between column_a and column_b
-dataframe_blobdata[['<column_a>', '<column_b>']].corr()
-```
+    # correlation between column_a and column_b
+    dataframe_blobdata[['<column_a>', '<column_b>']].corr()
+    ```