在 Pandas 数据框中使用 for 循环迭代列
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Iterating over columns with for loops in pandas dataframe
提问by JeremyD
I am trying to take a dataframe read in from CSV file, and generate scatter plots for each column within the dataframe. For example, I have read in the following with df=pandas.readcsv()
我正在尝试从 CSV 文件中读取一个数据框，并为数据框中的每一列生成散点图。例如，我阅读了以下内容df=pandas.readcsv()
Sample AMP ADP ATP
1A 239847 239084 987374
1B 245098 241210 988950
2A 238759 200554 921032
2B 230029 215408 899804
I would like to generate a scatter plot using sample as the x values, and the areas for each of the columns.
我想使用样本作为 x 值和每一列的面积来生成散点图。
I am using the following code with bokeh.plotting to plot each column manually
我使用以下代码与 bokeh.plotting 手动绘制每列
import pandas
from bokeh.plotting import figure, show
df = pandas.read_csv("data.csv")
p = figure(x_axis_label='Sample', y_axis_label='Peak Area', x_range=sorted(set(df['Sample'])))
p.scatter(df['Sample'], df['AMP'])
show(p)
This generates scatter plots successfully, but I would like to create a loop to generate a scatter plot for each column. In my full dataset, I have over 500 columns I would like to plot.
这会成功生成散点图，但我想创建一个循环来为每一列生成散点图。在我的完整数据集中，我想要绘制超过 500 列。
I have followed references for using df.iteritems and df.itertuples for iterating through dataframes, but I'm not sure how to get the output I want.
我遵循了使用 df.iteritems 和 df.itertuples 迭代数据帧的参考资料，但我不确定如何获得我想要的输出。
I have tried the following:
我尝试了以下方法：
for index, row in df.iteritems():
p = figure()
p.scatter(df['Sample'], df[row])
show(p)
I hit an error right away:
我马上就遇到了一个错误：
raise KeyError('%s not in index' % objarr[mask] KeyError: "['1A' '1B' '2A' '2B'] not in index
raise KeyError('%s not in index' % objarr[mask] KeyError: "['1A' '1B' '2A' '2B'] 不在索引中
Any guidance? Thanks in advance.
任何指导？提前致谢。
回答by piRSquared
iteritemsiterates over columns, not rows. But your real problem is when you are trying to df[row]instead of df[index]. I'd switch wording to columns and do this:
iteritems迭代列，而不是行。但你真正的问题是当你试图df[row]代替df[index]. 我会将措辞切换到列并执行以下操作：
for colname, col in df.iteritems():
p = figure()
p.scatter(df['Sample'], df[colname])
show(p)
