使用python做數(shù)據(jù)分析最關(guān)鍵的庫(kù)之一pandas侈咕，在數(shù)據(jù)處理最為常用磕潮，pandas中的函數(shù)分為如下幾大類

1. 輸入輸出類

讀入數(shù)據(jù)類

1. pd.read_csv(filename) -from a CSV file
2. pd.read_excel(filename) -from a excel file
3. pd.read_sql(query, connection_object) -Reads from a SQL table/database
4. pd.read_json(json_string) - Reads from a JSON formatted string, URL or file.
5. pd.read_html(url) - Parses an html URL, string or file and extracts tables to a list of dataframes
6. pd.read_clipboard()- Takes the contents of your clipboard and passes it to read_table()
7. pd.DataFrame(dict) -from a dict,keys for columns name, values for data as lists

輸出數(shù)據(jù)類函數(shù)

1. df.to _excel(filename) - Writes to an Excel file
2. df.to_csv(filename) Writes to a CSV file
3. df.to_sql(table_name, connection_object) -writes to a SQL table
4. df.to_json(filename) - Writes to a file in JSON format
5. df.to_html(filename)- Saves as an HTML table
6. df.to_clipboard() Writes to the clipboard

2. 生成測(cè)試數(shù)據(jù)

1. pd.DataFrame(np.random.rand(20,5)) -生成一個(gè)20行5列的隨機(jī)浮點(diǎn)數(shù)數(shù)據(jù)框
2. pd.Series(my_list) -由一個(gè)可迭代的my_list生成一個(gè)Series
3. df.index = pd.date_range('1900/1/30',periods = df.shape[0]) -增加一個(gè)時(shí)間序列的index

3. 查看數(shù)據(jù)總體情況

1. df.head(n)
2. df.tail(n)
3. df.shape() number of rows and columns
4. df.info()- Index, Datatype and Memory information
5. df.describe()- Summary statistics for numerical columns
6. s.value_counts(dropna = False) -查看唯一的值并計(jì)數(shù)
7. df.apply(pd.Series.value_couonts) - 對(duì)所有列唯一值計(jì)數(shù)

4. 數(shù)據(jù)選取

1. df[col] 作為Series返回col列
2. df[[col1, col2]] 返回多列數(shù)據(jù)翠胰，作為新數(shù)據(jù)框返回
3. s.iloc[0]- Selection by position
4. s.loc[0]- Selection by index
5. df.iloc[0,:] - First row
6. df.iloc[0,0]- First element of first column

5. 數(shù)據(jù)清洗

1. df.columns = ['a','b','c']- 重命名列名
2. pd.isnull() - 檢查空值容贝，返回布爾值數(shù)組
3. pd.notnull() - Opposite of s.isnull()
4. df.dropna()-刪除所有包含NA值的行 Drops all rows that contain null values
5. df.dropna(axis=1) - 刪除所有包含NA的列Drops all columns that contain null values
6. df.dropna(axis=1,thresh=n) - 刪除所有行中NA個(gè)數(shù)大于你的行 /Drops all rows have less than n non null values
7. df.fillna(x) - 用X填充NA /Replaces all null values with x
8. s.fillna(s.mean()) - 用均值填充NA /Replaces all null values with the mean (mean can be replaced with almost any function from the statistics section)
9. s.astype(float) -將Series的數(shù)據(jù)類型轉(zhuǎn)換為float / Converts the datatype of the series to float
10. s.replace(1,'one') - 用'one'代替1 /Replaces all values equal to 1 with 'one'
11. s.replace([1,3],['one','three']) - Replaces all 1 with 'one' and 3 with 'three'
12. df.rename(columns=lambda x: x + 1) - 對(duì)列進(jìn)行大規(guī)模重命名 /Mass renaming of columns
13. df.rename(columns={'old_name': 'new_ name'}) - 選擇性重命名列名 /Selective renaming
14. df.set_index('column_one') - 更改index /Changes the index
15. df.rename(index=lambda x: x + 1) - 大規(guī)模更改index
    /Mass renaming of index

6. 過濾、排序和分組

1. df[df[col] > 0.5] - Rows where the col column is greater than 0.5
2. df[(df[col] > 0.5) & (df[col] < 0.7)] - Rows where 0.7 > col > 0.5
3. df.sort_values(col1) -按col1升序排序 Sorts values by col1 in ascending order
4. df.sort_values(col2,ascending=False) -按col2降序排序 Sorts values by col2 in descending order
5. df.sort_values([col1,col2], ascending=[True,False]) - Sorts values by col1 in ascending order then col2 in descending order
6. df.groupby(col) - Returns a groupby object for values from one column
7. df.groupby([col1,col2]) - Returns a groupby object values from multiple columns
8. df.groupby(col1)[col2].mean()/df.groupby(col1).mean()[col2] - Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics section)
9. df.pivot_table(index=col1,values= [col2,col3],aggfunc=mean) - 創(chuàng)建一個(gè)透視表之景，根據(jù)col1分組斤富，計(jì)算col2,col3的均值 /Creates a pivot table that groups by col1 and calculates the mean of col2 and col3
10. df.groupby(col1).agg(np.mean) - Finds the average across all columns for every unique column 1 group
11. df.apply(np.mean) - Applies a function across each column
12. df.apply(np.max, axis=1) - Applies a function across each row

7. 統(tǒng)計(jì)函數(shù)

These can all be applied to a series as well.

1. df.describe() - Summary statistics for numerical columns
2. df.mean() - Returns the mean of all columns
3. df.corr() - Returns the correlation between columns in a DataFrame
4. df.count() - Returns the number of non-null values in each DataFrame column
5. df.max() - Returns the highest value in each column
6. df.min() - Returns the lowest value in each column
7. df.median() - Returns the median of each column
8. df.std() - Returns the standard deviation of each column

8. 連接數(shù)據(jù)

1. df1.append(df2) - Adds the rows in df1 to the end of df2 (columns should be identical)
2. pd.concat([df1, df2],axis=1) - Adds the columns in df1 to the end of df2 (rows should be identical)
3. df1.join(df2,on=col1,how='inner') - SQL-style joins the columns in df1 with the columns on df2 where the rows for col have identical values. how can be one of 'left', 'right', 'outer', 'inner'