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Python學習筆記: String 內建函數

以下內容摘自Python官方說明文件：

Built-in Types - Python 3.10.4 documentation

The following sections describe the standard types that are built into the interpreter. The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions. Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don't return a specific item, never return the collection instance itself but .

https://docs.python.org/zh-tw/3/library/stdtypes.html?highlight=replace#text-sequence-type-str

str.capitalize()

返回原字串的副本，其首個字元大寫，其餘為小寫。

x = "hello world"
print(x.capitalize())

OUTPUT:

Hello world

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str.count(sub[, start[, end]])

返回子字串 sub 在 [start, end] 範圍內非重疊出現的次數。 可選參數 start 與 end 會被解讀為切片標記法。

x = "hello world"
print(x.count("l"))

OUTPUT:

3

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str.startswith() 參考以下：

str.endswith(suffix[, start[, end]])

如果字串以指定的 suffix 結束返回 True，否則返回 False。 suffix 也可以為由多個供查找的尾碼構成的元組。 如果有可選項 start，將從所指定位置開始檢查。 如果有可選項 end，將在所指定位置停止比較。

x = "hello world"
print(x.endswith("rld"))

OUTPUT:

True

---

str.find(sub[, start[, end]])

返回子字串 sub 在 s[start:end] 切片內被找到的最小索引。 可選參數 start 與 end 會被解讀為切片標記法。 如果 sub 未被找到則返回 -1。

x = "hello world"
print(x.endswith("llo"))

OUTPUT:

True

x = "hello world"
print(x.endswith("lloe"))

OUTPUT:

-1

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Python學習筆記：Pandas 將DataFrame 轉換成 Dictionary，並依條件fillna

資料檔：

目標：將NaN以平均值填入

 import pandas as pd

import numpy as np

file = 'Fish.csv'

df = pd.read_csv(file)
print(len(df))
print(df.columns)
df.head()

159
Index(['Species', 'Weight', 'Length1', 'Length2', 'Length3', 'Height',
       'Width'],
      dtype='object')

Out[1]:

	Species	Weight	Length1	Length2	Length3	Height	Width
0	Bream	242.0	23.2	25.4	30.0	11.5200	4.0200
1	Bream	290.0	24.0	26.3	31.2	12.4800	4.3056
2	Bream	340.0	23.9	26.5	31.1	12.3778	4.6961
3	Bream	363.0	26.3	29.0	33.5	12.7300	4.4555
4	Bream	430.0	NaN	29.0	34.0	12.4440	5.1340

In [2]:

df_mean = df[['Species', 'Length1']].groupby('Species').mean()#.reset_index(drop=False)
df_mean

Out[2]:

	Length1
Species
Bream	30.417647
Parkki	18.727273
Perch	25.735714
Pike	42.476471
Roach	20.645000
Smelt	11.257143
Whitefish	28.800000

In [3]:

df_dict = df_mean.squeeze().to_dict()
df_dict

Out[3]:

{'Bream': 30.41764705882353,
 'Parkki': 18.727272727272727,
 'Perch': 25.735714285714288,
 'Pike': 42.476470588235294,
 'Roach': 20.645,
 'Smelt': 11.257142857142856,
 'Whitefish': 28.8}

In [4]:

for i in df['Species']:
    df.loc[(df['Species']==i) & (df['Length1'].isna()), 'Length1'] = df_dict[i]

df

Out[4]:

	Species	Weight	Length1	Length2	Length3	Height	Width
0	Bream	242.0	23.200000	25.4	30.0	11.5200	4.0200
1	Bream	290.0	24.000000	26.3	31.2	12.4800	4.3056
2	Bream	340.0	23.900000	26.5	31.1	12.3778	4.6961
3	Bream	363.0	26.300000	29.0	33.5	12.7300	4.4555
4	Bream	430.0	30.417647	29.0	34.0	12.4440	5.1340
...	...	...	...	...	...	...	...
154	Smelt	12.2	11.500000	12.2	13.4	2.0904	1.3936
155	Smelt	13.4	11.700000	12.4	13.5	2.4300	1.2690
156	Smelt	12.2	12.100000	13.0	13.8	2.2770	1.2558
157	Smelt	19.7	13.200000	14.3	15.2	2.8728	2.0672
158	Smelt	19.9	13.800000	15.0	16.2	2.9322	1.8792

159 rows × 7 columns