pandas高階操作總結

1.pandas中的列的分位數

# 檢視列的分位數

import pandas as pd
# set columns type
my_df['col'] = my_df['col'].astype(np.float64)
# computations for 4 quantiles : quartiles
bins_col = pd.qcut(my_df['col'], 4)
bins_col_label = pd.qcut(my_df['col'], 4).labels

# 多重聚合（組函式）

# columns settings
grouped_on = 'col_0' # ['col_0', 'col_2'] for multiple columns
aggregated_column = 'col_1'
### choice of aggregate functions
## on non-na values in the group
## - numeric choice :: mean, median, sum, std, var, min, max, prod
## - group choice :: first, last, count
# list of functions to compute
agg_funcs = ['mean', 'max']
# compute aggregate values
aggregated_values = my_df.groupby(grouped_on)[aggregated_columns].agg(agg_funcs)
# get the aggregate of group
aggregated_values.ix[group]

# 使用自定義函式進行聚合

# columns settings
grouped_on = ['col_0']
aggregated_columns = ['col_1']
def my_func(my_group_array):
return my_group_array.min() * my_group_array.count()
## list of functions to compute
agg_funcs = [my_func] # could be many
# compute aggregate values
aggregated_values = my_df.groupby(grouped_on)[aggregated_columns].agg(agg_funcs)

def top_n(group_df, col, n=2):

bests = group_df[col].value_counts()[:n]

return bests

# columns settings

grouped_on = 'col_0'

aggregated_column = 'col'

grouped = my_df.groupby(grouped_on)

5.移動平均

# 移動平均

import numpy as np
ret = np.cumsum(np.array(x), dtype=float)
ret[w:] = ret[w:] - ret[:-w]
result = ret[w - 1:] / w
# x: array-like
# window: int

# 組資料的基本資訊

# columns settings
grouped_on = 'col_0' # ['col_0', 'col_1'] for multiple columns
aggregated_column = 'col_1'
### choice of aggregate functions
## on non-na values in the group
## - numeric choice : mean, median, sum, std, var, min, max, prod
## - group choice : first, last, count
## on the group lines
## - size of the group : size
aggregated_values = my_df.groupby(grouped_on)[aggregated_column].mean()
aggregated_values.name = 'mean'
# get the aggregate of group
aggregated_values.ix[group]

# 資料組的遍歷

# columns settings
grouped_on = 'col_0' # ['col_0', 'col_1'] for multiple columns
grouped = my_df.groupby(grouped_on)
i = 0
for group_name, group_dataframe in grouped:
if i > 10:
break
i += 1
print(i, group_name, group_dataframe.mean()) ## mean on all numerical columns

# 最大互資訊數

import numpy as np
matrix = np.transpose(np.array(x)).astype(float)
mic_result = 
for i in matrix[1:]:
mine.compute_score(t_matrix[0], i)
return mic_result

9.pearson相關係數

import numpy as np

matrix = np.transpose(np.array(x))
np.corrcoef(matrix[0], matrix[1])[0, 1]
# x: array-like
#

# 自定義聚合函式

def zscore(x):
return (x - x.mean()) / x.std()
my_df['zscore_col'] = my_df.groupby(grouped_on)[aggregated_column].transform(zscore)

# 標準聚合使用groupby

# columns settings
grouped_on = 'col_1'
aggregated_column = 'col_0'
### choice of aggregate functions
## on non-na values in the group
## - numeric choice : mean, median, sum, std, var, min, max, prod
## - group choice : first, last, count
my_df['aggregate_values_on_col'] = my_df.groupby(grouped_on)[aggregated_column].transform(lambda v: v.mean())

# 使用自定義函式設值

def to_log(v):
try:
return log(v)
except:
return np.nan
my_df['new_col'] = my_df['col_0'].map(to_log)

# 使用複雜的函式設值

import numpy as np
def complex_formula(col0_value, col1_value):
return "%s (%s)" % (col0_value, col1_value)
my_df['new_col'] = np.vectorize(complex_formula)(my_df['col_0'], my_df['col_1'])

14.使用字典dict設值

# 使用字典dict設值

gender_dict=
df['gender'] = df['gender'].map(gender_dict)

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