擅长:python、mysql、java
<p>我的特性是一个<em><code><class 'pandas.core.frame.DataFrame'></code></em></p>
<p><em>目标</em>是一个<em><code><classpandas.core.series.Series'></code></em></p>
<p>但许多函数都在这两种数据结构上工作。我甚至可以将两者都传递给matplotlib函数。在</p>
<p>在研究差异时,我发现它已经被解释了<a href="https://stackoverflow.com/questions/26047209/what-is-the-difference-between-a-pandas-series-and-a-single-column-dataframe">here</a></p>
<pre><code>import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
california_housing_dataframe = pd.read_csv("https://dl.google.com/mlcc/mledu-datasets/california_housing_train.csv", sep=",")
# Define the input feature: total_rooms.
my_feature = california_housing_dataframe[["total_rooms"]]
print(type(my_feature))
# Configure a numeric feature column for total_rooms.
feature_columns = [tf.feature_column.numeric_column("total_rooms")]
targets = california_housing_dataframe["median_house_value"]
print(type(targets))
print( my_feature.describe())
print( targets.describe())
print( my_feature.head())
print( targets.head())
print( my_feature.max())
print( targets.max())
</code></pre>