df2.dropna(axis=0, how='any', subset=[u'ToC'], inplace=True)
把在ToC列有缺失值的行去掉
补充：还可以用df.fillna()来把缺失值替换为某个特殊标记
df = df.fillna("missing") # 用字符串替代
df = df.fillna(df.mean()) # 用均值或者其它描述性统计值替代
print df.duplicated(['name']).value_counts() # 如果不指定列，默认会判断所有列 """ 输出: False 11118 True 664 表示有664行是重复的 """
duplicated()方法返回一个布尔型的Series，显示各行是否为重复行，非重复行显示为False，重复行显示为True
value_counts()方法统计数组或序列所有元素出现次数，对某一列统计可以直接用df.column_name.value_counts()
df.drop_duplicates(['name'], keep='last', inplace=True)
"""
keep : {‘first’, ‘last’, False}, default ‘first’
first : Drop duplicates except for the first occurrence.
last : Drop duplicates except for the last occurrence.
False : Drop all duplicates.
"""
result = pd.merge(left, right, on='name', how='inner')
"""
其它参数：
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)
Examples
>>> A >>> B
lkey value rkey value
0 foo 1 0 foo 5
1 bar 2 1 bar 6
2 baz 3 2 qux 7
3 foo 4 3 bar 8
>>> A.merge(B, left_on='lkey', right_on='rkey', how='outer')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 4 foo 5
2 bar 2 bar 6
3 bar 2 bar 8
4 baz 3 NaN NaN
5 NaN NaN qux 7
"""
其它参考：Merge, join, and concatenate
def concat_by_row(data_dir, fout):
dfs = []
for filename in os.listdir(data_dir):
dfs.append(pd.read_excel(os.path.join(data_dir, filename)))
print(dfs[-1].shape, filename)
df = pd.concat(dfs, axis=0, ignore_index=True) # axis=0按行拼接；axis=1按列拼接
print(df.shape)
df.to_excel(fout, index=False)
bool_arr = df.name.notnull()
print bool_arr.value_counts()
for idx, value in bool_arr.iteritems():
if not value:
print '\n', idx, value
print df.iloc[idx]
res = {'name':[], 'buss':[], 'label':[]}
with codecs.open(fname, encoding='utf8') as fr:
for idx, line in enumerate(fr):
item = json.loads(line)
res['name'].append(item['name'])
res['buss'].append(item['buss'])
res['label'].append(item['label'])
df = pd.DataFrame(res, columns=['name', 'buss', 'label'])
df.to_csv('data/xxx.csv', index=False, encoding='utf-8')
import pandas as pd
def dataframe_read_and_write(fin, fout): # 读取fin文件，添加一列"新应答"
df = pd.read_excel(fin)
# df = read_csv(fin, encoding='utf-8')
print df.head()
fields = [u"序号", u"问题描述", u"原始应答", u"新应答"]
data_out = defaultdict(list)
for idx, row in df.iterrows():
try:
row = row.to_dict()
new_answer = "xxxxxx"
for field in fields[:-1]:
data_out[field].append(row[field])
data_out[fields[-1]].append(new_answer)
except Exception as error:
print "Error line", idx, error
df_out = pd.DataFrame(data_out, columns=fields)
df_out.to_excel(fout, sheet_name="Sheet1", index=False, header=True)
# df_out.to_csv(fout, index=False, encoding="utf-8")
if __name__ == '__main__':
dataframe_read_and_write(fin="data/tmp.xlsx", fout="data/tmp_out.xlsx")
import pandas as pd
writer = pd.ExcelWriter('foo.xlsx')
df.to_excel(writer, 'Data 0')
df.to_excel(writer, 'Data 1')
writer.save()
import pandas
from openpyxl import load_workbook
def add_new_sheet(df, fout, sheet_name='Sheet1', columns=None):
if fout and os.path.exists(fout):
book = load_workbook(fout)
writer = pd.ExcelWriter(fout, engine='openpyxl')
writer.book = book
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
else:
writer = pd.ExcelWriter(fout)
df.to_excel(writer, sheet_name=sheet_name, columns=columns, index=False)
writer.save()
add_new_sheet(df, fout='Masterfile.xlsx', sheet_name="Main", columns=['Diff1', 'Diff2'])
参考：官方解决方案https://github.com/pandas-dev/pandas/issues/3441
import xlrd
workbook = xlrd.open_workbook(fin)
for sheet in workbook.sheets():
df = pd.read_excel(fin, sheet_name=sheet.name, index_col=None)
def sort_dataframe(df, fields_to_sort, fout=None):
df = df.sort_values(by=fields_to_sort, ascending=True)
if fout:
df.to_excel(fout, index=False)
return df
df = pd.read_excel(data_file)
sort_dataframe(df, fields_to_sort=["column_A", "column_B"], fout=data_file)
df = pd.read_excel(data_file) # note: index改变，需要从文件重新读取，才会是有序的，后面遍历df的时候才不会出问题
print(df.head(10))
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(index=str, columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
>>> df.rename(index=str, columns={"A": "a", "C": "c"})
a B
0 1 4
1 2 5
2 3 6
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
参数说明：
df.rename()参数说明
在知道列名字的情况下，df.loc[index,column] 选取指定行，列的数据
# df.loc[index, column_name],选取指定行和列的数据 df.loc[0,'name'] # 'Snow' df.loc[0:2, ['name','age']] #选取第0行到第2行，name列和age列的数据, 注意这里的行选取是包含下标的。 df.loc[[2,3],['name','age']] #选取指定的第2行和第3行，name和age列的数据 df.loc[df['gender']=='M','name'] #选取gender列是M，name列的数据 df.loc[df['gender']=='M',['name','age']] #选取gender列是M，name和age列的数据
在column name特别长或者index是时间序列等各种不方便输入的情况下，可以用iloc (i = index), iloc完全用数字来定位 iloc[row_index, column_index]
df.iloc[0,0] #第0行第0列的数据，'Snow' df.iloc[1,2] #第1行第2列的数据，32 df.iloc[[1,3],0:2] #第1行和第3行，从第0列到第2列（不包含第2列）的数据 df.iloc[1:3,[1,2] #第1行到第3行（不包含第3行），第1列和第2列的数据
更多参考：
https://blog.csdn.net/yoonhee/article/details/76168253
if str(line["col_a"]).strip() == "nan":
pass
df["col_a"] = df["col_a"].replace({"b": "C", "e": "G"})
更多参考：https://jingyan.baidu.com/article/454316ab4d0e64f7a6c03a41.html
df2 = df[df["col_a"] == "cc"] # 等于某个值 df3 = df[df["col_a"].isin(["bb", "cc", "ee"])] # 取值在某个范围内
更多参考：https://jingyan.baidu.com/article/0eb457e508b6d303f0a90572.html
# df = pd.read_csv("../../data/data_part1.txt", sep="$")
df = pd.read_csv("data/data_part1.csv", low_memory=False)
# 数据概览
df.info()
df.describe() # ==> 只显示float型维度的[count, mean, std, min]等统计信息, 例如0108, 3816, 2453, 0112, 2428, 2304
# 数据查看
df.head(n=5) # 查看开头几行, 默认n=5
df.tail(n=5) # 查看末尾几行, 默认n=5
df.shape # 查看行列维度
df.columns # 查看列名和列数
df.dtypes # 查看数据类型 ==> 可以看到哪些维度的数值是object型/float型
df["0108"].hist() # 查看变量分布
df["0108"].unique() # 查看有哪些取值
df["0108"].value_counts() # 查看这一列的值统计
# 缺失值统计
df.isnull().sum() # 查看每一列缺失值情况
df["n_null"] = df.isnull().sum(axis=1) # 查看每一行缺失值情况
# 缺失值填充
mode_df = df.fillna(df.mode().iloc[0], inplace=True) # 用众数填充
median_df = df.fillna(df.median()) # 用中位数填充
df["0108"][df.vid.isnull()] = "0" # 对某一列填充
(step 1) pip install xlsxwriter
(step 2) df.to_excel(fout, engine="xlsxwriter")
writer = pd.ExcelWriter(fout, engine="xlsxwriter", options={'strings_to_urls': False})
df.to_excel(writer, index=False)
writer.save()
参考：
