Fix up CSV data tutorial: fix typos; link to pandas.DataFrame docs …

…on first usage, rather than second; add transition sentence to mixed data types section.

PiperOrigin-RevId: 572711158

site/en/tutorials/load_data/csv.ipynb

@@ -120,7 +120,7 @@
         "id": "ny5TEgcmHjVx"
       },
       "source": [
-        "For any small CSV dataset the simplest way to train a TensorFlow model on it is to load it into memory as a pandas Dataframe or a NumPy array.\n"
+        "For any small CSV dataset the simplest way to train a TensorFlow model on it is to load it into memory as a [pandas `DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) or a NumPy array.\n"
       ]
     },
     {
@@ -132,9 +132,9 @@
         "A relatively simple example is the [abalone dataset](https://archive.ics.uci.edu/ml/datasets/abalone).\n",
         "\n",
         "* The dataset is small.\n",
-        "* All the input features are all limited-range floating point values.\n",
+        "* All the input features are limited-range floating point values.\n",
         "\n",
-        "Here is how to download the data into a [pandas `DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html):"
+        "Here is how to download the data into a `DataFrame`:"
       ]
     },
     {
@@ -355,6 +355,8 @@
       "source": [
         "## Mixed data types\n",
         "\n",
+        "In the previous sections, you worked with a dataset where all the features were limited-range floating point values. But not all datasets are limited to a single data type.\n",
+        "\n",
         "The \"Titanic\" dataset contains information about the passengers on the Titanic. The nominal task on this dataset is to predict who survived.\n",
         "\n",
         "![The Titanic](images/csv/Titanic.jpg)\n",
@@ -903,7 +905,7 @@
       "source": [
         "### From a single file\n",
         "\n",
-        "So far this tutorial has worked with in-memory data. `tf.data` is a highly scalable toolkit for building data pipelines, and provides a few functions for dealing loading CSV files. "
+        "So far this tutorial has worked with in-memory data. `tf.data` is a highly scalable toolkit for building data pipelines, and provides a few functions for loading CSV files. "
       ]
     },
     {
@@ -1373,7 +1375,7 @@
         "id": "3jiGZeUijJNd"
       },
       "source": [
-        "So far this tutorial has focused on the highest-level utilities for reading csv data. There are other two APIs that may be helpful for advanced users if your use-case doesn't fit the basic patterns.\n",
+        "So far this tutorial has focused on the highest-level utilities for reading csv data. There are two other APIs that may be helpful for advanced users if your use-case doesn't fit the basic patterns.\n",
         "\n",
         "* `tf.io.decode_csv`: a function for parsing lines of text into a list of CSV column tensors.\n",
         "* `tf.data.experimental.CsvDataset`: a lower-level CSV dataset constructor.\n",