将多个CSV文件读取到Python Pandas Dataframe中 [英] Reading Multiple CSV Files into Python Pandas Dataframe

问题描述

该问题背后的一般用例是将目标目录中的多个CSV日志文件读取到单个Python Pandas DataFrame中，以进行快速周转统计分析&图表.利用Pandas vs MySQL的想法是全天定期进行数据导入或附加+统计分析.

The general use case behind the question is to read multiple CSV log files from a target directory into a single Python Pandas DataFrame for quick turnaround statistical analysis & charting. The idea for utilizing Pandas vs MySQL is to conduct this data import or append + stat analysis periodically throughout the day.

以下脚本尝试将所有CSV(相同文件布局)文件读入单个Pandas数据框&添加与每个读取的文件关联的年份列.

The script below attempts to read all of the CSV (same file layout) files into a single Pandas dataframe & adds a year column associated with each file read.

# Assemble all of the data files into a single DataFrame & add a year field
# 2010 is the last available year
years = range(1880, 2011)

for year in years:
    path ='C:\\Documents and Settings\\Foo\\My Documents\\pydata-book\\pydata-book-master`\\ch02\\names\\yob%d.txt' % year
    frame = pd.read_csv(path, names=columns)

    frame['year'] = year
    pieces.append(frame)

# Concatenates everything into a single Dataframe
names = pd.concat(pieces, ignore_index=True)

# Expected row total should be 1690784
names
<class 'pandas.core.frame.DataFrame'>
Int64Index: 33838 entries, 0 to 33837
Data columns:
name      33838  non-null values
sex       33838  non-null values
births    33838  non-null values
year      33838  non-null values
dtypes: int64(2), object(2)

# Start aggregating the data at the year & gender level using groupby or pivot
total_births = names.pivot_table('births', rows='year', cols='sex', aggfunc=sum)
# Prints pivot table
total_births.tail()

Out[35]:
sex     F   M
year        
2010    1759010     1898382

推荐答案

DataFrame实例上的append方法的功能与列表实例上的append方法的功能不同. Dataframe.append()不会就地发生，而是返回一个新对象.

The append method on an instance of a DataFrame does not function the same as the append method on an instance of a list. Dataframe.append() does not occur in-place and instead returns a new object.

years = range(1880, 2011)

names = pd.DataFrame()
for year in years:
    path ='C:\\Documents and Settings\\Foo\\My Documents\\pydata-book\\pydata-book-master`\\ch02\\names\\yob%d.txt' % year
    frame = pd.read_csv(path, names=columns)

    frame['year'] = year
    names = names.append(frame, ignore_index=True)

，或者您可以使用concat:

years = range(1880, 2011)

names = pd.DataFrame()
for year in years:
    path ='C:\\Documents and Settings\\Foo\\My Documents\\pydata-book\\pydata-book-master`\\ch02\\names\\yob%d.txt' % year
    frame = pd.read_csv(path, names=columns)

    frame['year'] = year
    names = pd.concat(names, frame, ignore_index=True)

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