In [66]: pd.read_csv(StringIO(data), usecols=lambda x: x not in ["a", "c"]) Out[66]: b d 0 2 foo 1 5 bar 2 8 baz 在这种情况下,可调用对象指定我们从输出中排除“a”和“c”列。 注释和空行 忽略行注释和空行 如果指定了comment参数,则完全注释的行将被忽略。默认情况下,完全空白...
df[(df.price >4) & (df.fruit =="grape")] # Filter using query df.query("price > 4 & fruit == 'grape'") 💡 12:逆透视数据表 如果要将 DataFrame 从宽表格式转换为长表格式,可以使用pandas.melt()。 如下例,我们可以使用pandas.melt()将多列(“Aldi”、“Walmart”、“Costco”)转换为一...
query('i0 == "b" & i1 == "b"') # 多索引查询方法 2 # 取多索引中指定级别的所有不重复值 df.index.get_level_values(2).unique() # 去掉为零小数,12.00 -> 12 df.astype('str').applymap(lambda x: x.replace('.00', '')) # 插入数据,在第三列加入「两倍」列 df.insert(3, '...
do not use the index values along the concatenation axis. Theresulting axis will be labeled 0, ..., n - 1. This is useful if you areconcatenating objects where the concatenation axis does not havemeaningful indexing information. Note the index values on the otheraxes are still respected...
https://jakevdp.github.io/PythonDataScienceHandbook/03.12-performance-eval-and-query.html High-Performance Pandas: eval() and query() <Working with Time Series|Contents|Further Resources> As we've already seen in previous sections, the power of the PyData stack is built upon the ability of Nu...
# Filter using query df.query("price > 4 & fruit == 'grape'") 12:逆透视数据表 如果要将 DataFrame 从宽表格式转换为长表格式,可以使用pandas.melt()。 如下例,我们可以使用pandas.melt()将多列(“Aldi”、“Walmart”、“Costco”)转换为一列(“store”)的值。 import pandas as pd df = pd.Da...
read_csv("sample.csv") # Initialize PandasQueryEngine with the DataFrame query_engine = PandasQueryEngine(df=df, verbose=True) # Update prompts if needed new_prompt = PromptTemplate( """\ You are working with a pandas dataframe in Python. The name of the dataframe is `df`. This is ...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more - pandas-dev/pandas
query('i0 == "b" & i1 == "b"') # 多索引查询方法 2 # 取多索引中指定级别的所有不重复值 df.index.get_level_values(2).unique() # 去掉为零小数,12.00 -> 12 df.astype('str').applymap(lambda x: x.replace('.00', '')) # 插入数据,在第三列加入「两倍」列 df.insert(3, '...
df2=df.query("Courses == 'Spark'") # Using variable value='Spark' df2=df.query("Courses == @value") # Inpace df.query("Courses == 'Spark'",inplace=True) # Not equals, in & multiple conditions df.query("Courses != 'Spark'") ...