“Label_3”值。
返回相等行以及其他几个条件的示例。作者 [3] 的截图。
'df.tail()'以查看返回的行是否符合我的预期。
然后我返回行，其中一列的值等于另一列的值。您也可以使用相同的逻辑，但将其反转以查看您指定的列的哪些值彼此不相等。接下来，您可以比较一列的值是否大于另一列的值，反之亦然。
“@”方法保存要比较的值。对于下面的示例，我查看了要比较的一列的平均值。
“&”在一行代码中执行多个条件。
# All the python code below for use: # import library import pandas as pd # read in your dataframe df = pd.read_csv('/Users/example.csv') # write out new rows rows = [pd.Series([100, 100, 20,'Blue','Label_1'], index=df.columns), pd.Series([100, 80, 60,'Blue','Label_1'], index=df.columns), pd.Series([80, 60, 100,'Blue','Label_1'], index=df.columns)] # append the multiple rows 追加多行 new_df = df.append(rows , ignore_index=True) # check the newest 3 rows you made 检索创建的最新 3 行 new_df.tail(3) # return rows where values from one column equal that of another # they do not for this comparison new_df.query('Confirmed_Test == Confirmed_New') # return rows where values from one column equal that of another # they do for this comparison new_df.query('Confirmed_Test == Confirmed_Recovery') # return rows where values from one column do not equal that of another # they do for this comparison new_df.query('Confirmed_New != Confirmed_Recovery').head() # return rows where values from one column are bigger than that of another 返回一列值大于另一列值的行 new_df.query('Confirmed_New > Confirmed_Recovery').head() # see which rows where the 'Confirmed_New' values # are greater than the mean of the total column # use the '@' to reference 'cn_mean'
# 查看“Confirmed_New”值所在的行 # 大于总列的平均值 # 使用'@'来引用'cn_mean'
cn_mean = new_df['Confirmed_New'].mean() new_df.query('Confirmed_New > @cn_mean').head() # multiple conditions example cn_min = new_df['Confirmed_New'].min() cn_max = new_df['Confirmed_New'].max() new_df.query('Confirmed_New > @cn_min & Confirmed_New < @cn_max').head() # text conditions # use double quotes for matching on the string value # all the rows returned have 'Blue' for the 'Text_Feature' column new_df.query('Text_Feature == "Blue"').head() # return rows which have a 'Blue' value, and a 'Confirmed_Recovery' that is greater # than the mean of 'Confirmed_Recovery' itself, while also satisfying the 'Label_3' value # only 3 return here (did not print out head()) cr_mean = new_df['Confirmed_Recovery'].mean() new_df.query('Text_Feature == "Blue" & Confirmed_Recovery > @cr_mean & Text_Predictor == "Label_3"')