Question

我知道在arcpy中是可能的。了解是否可能在熊猫中发生。

我有以下

develop

我需要选择FY等于或大于2016的所有P.PIN，P.RAD和其他以E开头的物种，并将其放入新的数据框中。

如何完成此任务。我能选择P.PIN和P.RAD，但其他都以E开头；

    number_of_rows = self.cur.execute("SELECT * FROM account")
    print(number_of_rows)
    self.cur.execute("select * FROM account")
    if number_of_rows == 1:
        row = self.cur.fetchone()
        self.lineEdit_1.setText(str(row[0]))
        self.lineEdit_2.setText(str(row[1]))
        self.lineEdit_3.setText(str(row[2]))
        self.lineEdit_4.setText(str(row[3]))
    elif number_of_rows == 2:
        row = self.cur.fetchone()
        self.lineEdit_1.setText(str(row[0]))
        self.lineEdit_2.setText(str(row[1]))
        self.lineEdit_3.setText(str(row[2]))
        self.lineEdit_4.setText(str(row[3]))
        row2 = self.cur.fetchone()
        self.lineEdit_5.setText(str(row2[0]))
        self.lineEdit_6.setText(str(row2[1]))
        self.lineEdit_7.setText(str(row2[2]))
        self.lineEdit_8.setText(str(row2[3]))

    elif number_of_rows == 3:
        row = self.cur.fetchone()
        self.lineEdit_1.setText(str(row[0]))
        self.lineEdit_2.setText(str(row[1]))
        self.lineEdit_3.setText(str(row[2]))
        self.lineEdit_4.setText(str(row[3]))
        row2 = self.cur.fetchone()
        self.lineEdit_5.setText(str(row2[0]))
        self.lineEdit_6.setText(str(row2[1]))
        self.lineEdit_7.setText(str(row2[2]))
        self.lineEdit_8.setText(str(row2[3]))
        row3 = self.cur.fetchone()
        self.lineEdit_9.setText(str(row3[0]))
        self.lineEdit_10.setText(str(row3[1]))
        self.lineEdit_11.setText(str(row3[2]))
        self.lineEdit_12.setText(str(row3[3]))

我们将非常感谢您的帮助。

Answer 1

逐步进行。但是，您也可以在np.where()内组合逻辑，只想证明所有条件都已完成。

首先将您的df['FY']值转换为int类型，以便我们可以使用大于（>）运算符。

>>> df['FY'] = df['FY'].astype(int)

>>> df['flag'] = np.where(df['Species'].isin(['P.PIN', 'P.RAD']), ['Take'], ['Remove'])
>>> df
   Species    FY    flag
0    P.PIN  2002    Take
1    P.PIN  2016    Take
2    V.FOG  2018  Remove
3    V.KOP  2010  Remove
4    E.MON  2009  Remove
5    E.CLA  2019  Remove
6    E.KLI  2017  Remove
7    D.FGH  2016  Remove
8    W.ERT  2018  Remove
9    S.MIX  2018  Remove
10   P.PIN  2016    Take

>>> df['flag'] = np.where((df['FY'] > 2016) & (df['Species'].str.startswith('E')), ['Take'], df['flag'])
>>> df
   Species    FY    flag
0    P.PIN  2002    Take
1    P.PIN  2016    Take
2    V.FOG  2018  Remove
3    V.KOP  2010  Remove
4    E.MON  2009  Remove
5    E.CLA  2019    Take
6    E.KLI  2017    Take
7    D.FGH  2016  Remove
8    W.ERT  2018  Remove
9    S.MIX  2018  Remove
10   P.PIN  2016    Take

>>> new_df = df[df['flag'].isin(['Take'])][['Species', 'FY']]
>>> new_df
   Species    FY
0    P.PIN  2002
1    P.PIN  2016
5    E.CLA  2019
6    E.KLI  2017
10   P.PIN  2016

希望这会有所帮助：D

按列值选择

1 个答案: