pandas 如何使用pandas python获取数据框中每列的最大长度
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How to get maximum length of each column in the data frame using pandas python
提问by singularity2047
I have a data frame where most of the columns are varchar/object type. Length of the column varies a lot and could be anything within the range of 3 - 1000+ . Now, for each column, I want to measure maximum length.
我有一个数据框，其中大部分列都是 varchar/object 类型。列的长度变化很大，可以是 3 - 1000+ 范围内的任何值。现在，对于每一列，我想测量最大长度。
I know how to calculate maximum length for a col. If its varchar then:
我知道如何计算 col 的最大长度。如果它的 varchar 则：
max(df.char_col.apply(len))
and if its number (float8 or int64) then:
如果它的数字（float8 或 int64）则：
max(df.num_col.map(str).apply(len))
But my dataframe has hundreds of column and I want to calculate maximum length for all columns at the same time. The problem for that is, there are different data types, and I dont know how to do all at once.
但是我的数据框有数百列，我想同时计算所有列的最大长度。问题是，有不同的数据类型，我不知道如何一次完成。
So Question 1: How to get maximum column length for each columns in the data frame
所以问题1：如何获得数据框中每一列的最大列长
Now I am trying to do that only for varchar/object type columns using following code:
现在我正在尝试使用以下代码仅对 varchar/object 类型列执行此操作：
xx = df.select_dtypes(include = ['object'])
for col in [xx.columns.values]:
maxlength = [max(xx.col.apply(len))]
I selected only object type columns and tried to write a for loop. But its not working. probably using apply() within for loop is not a good idea.
我只选择了对象类型列并尝试编写一个 for 循环。但它不工作。可能在 for 循环中使用 apply() 不是一个好主意。
Question 2: How to get maximum length of each column for only object type columns
问题 2：如何仅获取对象类型列的每列的最大长度
Sample data frame:
示例数据框：
d1 = {'name': ['john', 'tom', 'bob', 'rock', 'jimy'], 'DoB': ['01/02/2010', '01/02/2012', '11/22/2014', '11/22/2014', '09/25/2016'], 'Address': ['NY', 'NJ', 'PA', 'NY', 'CA'], 'comment1': ['Very good performance', 'N/A', 'Need to work hard', 'No Comment', 'Not satisfactory'], 'comment2': ['good', 'Meets Expectation', 'N', 'N/A', 'Incompetence']}
df1 = pd.DataFrame(data = d1)
df1['month'] = pd.DatetimeIndex(df1['DoB']).month
df1['year'] = pd.DatetimeIndex(df1['DoB']).year
回答by jpp
One solution is to use numpy.vectorize. This may be more efficient than pandas-based solutions.
一种解决方案是使用numpy.vectorize. 这可能比pandas基于的解决方案更有效。
You can use pd.DataFrame.select_dtypesto select objectcolumns.
您可以使用pd.DataFrame.select_dtypes来选择object列。
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': ['abc', 'de', 'abcd'],
'B': ['a', 'abcde', 'abc'],
'C': [1, 2.5, 1.5]})
measurer = np.vectorize(len)
Max length for all columns
所有列的最大长度
res1 = measurer(df.values.astype(str)).max(axis=0)
array([4, 5, 3])
Max length for object columns
对象列的最大长度
res2 = measurer(df.select_dtypes(include=[object]).values.astype(str)).max(axis=0)
array([4, 5])
Or if you need output as a dictionary:
或者，如果您需要将输出作为字典：
res1 = dict(zip(df, measurer(df.values.astype(str)).max(axis=0)))
{'A': 4, 'B': 5, 'C': 3}
df_object = df.select_dtypes(include=[object])
res2 = dict(zip(df_object, measurer(df_object.values.astype(str)).max(axis=0)))
{'A': 4, 'B': 5}
回答by alif
Some great answers here and I would like to contribute mine
这里有一些很棒的答案，我想贡献我的
Solution:
解决方案：
dict([(v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max())for v in df.columns.values])
Explanation:
说明：
#convert tuple to dictionary
dict(
[
#create a tuple such that (column name, max length of values in column)
(v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max())
for v in df.columns.values #iterates over all column values
])
Sample output
样本输出
{'name': 4, 'DoB': 10, 'Address': 2, 'comment1': 21, 'comment2': 17}
回答by Osmond Bishop
Select only object type columns
仅选择对象类型列
df2 = df1[[x for x in df1 if df1[x].dtype == 'O']]
Get the maximum length in each column
获取每列的最大长度
max_length_in_each_col = df2.applymap(lambda x: len(x)).max()
回答by Azhar Ansari
I tried numpy.vectorizebut it gave 'Memory Error'for huge dataframe.
我尝试了numpy.vectorize但它为巨大的数据帧提供了“内存错误”。
The below code worked perfectly for me. It will give you a list of maximum lengths for each column in an excel spreadsheet (read into a dataframe using pandas)
下面的代码非常适合我。它将为您提供 Excel 电子表格中每列的最大长度列表（使用 Pandas 读入数据框）
import pandas as pd
xl = pd.ExcelFile('sample.xlsx')
df = xl.parse('Sheet1')
maxColumnLenghts = []
for col in range(len(df.columns)):
maxColumnLenghts.append(max(df.iloc[:,col].astype(str).apply(len)))
print('Max Column Lengths ', maxColumnLenghts)
回答by MSallal
You can use min max after using str and len method
您可以在使用 str 和 len 方法后使用 min max
df["A"].str.len().max()
df["A"].str.len().min()
df["Column Name"].str.len().max()
df["Column Name"].str.len().min()
