size(): Compute group sizes count(): Compute count of group std(): Standard deviation of groups var(): Compute variance of groups sem(): Standard error of the mean of groups describe(): Generates descriptive st
常用聚合方法如下: mean(): Compute mean of groups sum(): Compute sum of group values size(): Compute group sizes count(): Compute count of group std(): Standard deviation of groups var(): Compute variance of groups sem(): Standard error of the mean of groups describe(): Generates desc...
size(): Compute group sizes count(): Compute count of group std(): Standard deviation of groups var(): Compute variance of groups sem(): Standard error of the mean of groups describe(): Generates descriptive statistics first(): Compute first of group values last(): Compute last of group ...
# create a list of the values we want to assign for each condition values = ['tier_4', 'tier_3', 'tier_2', 'tier_1'] # create a new column and use np.select to assign values to it using our lists as arguments df['tier'] = np.select(conditions, values) 删除行/列 df.drop...
("Max Value:", max_value) print("Total Sum:", total_sum) print("Standard Deviation:", std_dev) # 可视化统计结果(例如使用matplotlib) import matplotlib.pyplot as plt df['value_column'].hist(bins=10) plt.title('Histogram of Value Column') plt.xlabel('Value') plt.ylabel('Frequency') ...
Standard Deviation: df.std() – standard deviation of each column Max: df.max() – highest value in each column Min: df.min() – lowest value in each column Count: df.count() – number of non-null values in each DataFrame column Describe: df.describe() – Summary statistics for numer...
columns Returns the column labels of the DataFrame combine() Compare the values in two DataFrames, and let a function decide which values to keep combine_first() Compare two DataFrames, and if the first DataFrame has a NULL value, it will be filled with the respective value from the second...
#滑动统计函数:滑动最小值(Rolling Minimum) import pandas as pd df['Min'] = df['column'].rolling(window=7).min() df columnMASumMaxMin 0 1 NaN NaN NaN NaN 1 2 NaN NaN NaN NaN 2 3 2.0 NaN NaN NaN 3 4 3.0 NaN NaN NaN 4 5 4.0 15.0 NaN NaN 5 6 5.0 20.0 NaN NaN 6 7 6.0...
# Groupby the quality column using aggregate # value of mean and std qual=df.groupby("quality").agg([np.mean,np.std]) qual=qual['insert'] qual.plot(kind="barh",y="mean",legend=False, xerr="std",title="Quality",color='green') ...
# Calculate Standard Deviation # of 'Fare' column std=dataset['Fare'].std() # Print standard deviation print(std) 输出: 49.693428597180905 6.最大: 使用DataFrame/Series.max() 方法计算最大值。 语法:DataFrame/Series.max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs...